The Aging Wimbledon Men’s Draw

Italian translation at settesei.it

Men’s tennis is getting older, and the drift toward middle age is evident at Wimbledon this week.

Of the 128 men in the main draw, 34 are at least 30 years old, while only two are in their teens.  This is just the latest step in a trend that has been evident for at least a decade.

The 34 30-somethings are not just a modern-day record–the number blows recent years out of the water.  Last year’s main draw had 24 30-somethings, and that was the highest such total since 1979.  Teenagers have been on the wane for years–there have only been two in the main draw in each of the last four years, but as recently as 2001, there were eight.  In several years in the late 80s and early 90s, there were more teenagers than 30-year-olds.

Whatever the explanation for this–and there are many possible ones–it’s clear that something is going on.  It takes longer than it ever has for a young rising star to establish himself on tour, and top players are able to stay healthy and competitive for as long as ever before.

After the jump, find a table with more detailed results.

Continue reading The Aging Wimbledon Men’s Draw

2012 Wimbledon Men’s Projections

Here are my projections for this year’s Wimbledon men’s draw.  Djokovic is far and away the favorite now that we’ve moved away from clay.  Federer comes in a close third behind Nadal, helped in part by what is probably the easiest of the four quarters.

Intuitively, these numbers seem about right, especially for the top players.  But a few developments in the ATP recently have exposed some gaps in my ranking system.  Brian Baker’s quick ascendance has yet to do much for him in my system, in part because he hasn’t played very much top-level matches.  But after his performance in Nice, it seems wrong to give him less than a 35% chance against a journeyman like Rui Machado.

The other head-scratcher is Tommy Haas.  After winning Halle, my system isn’t giving him much credit, in large part because he’s 34. Since players start going downhill by age 26, a player’s rate of decline in his mid-30s would generally be staggering.   But, of course, most players are gone by then.  If someone like Haas is still playing (and winning), he probably isn’t subject to exactly the same laws.  Perhaps 34-year-olds on tour are rare enough that it isn’t all that important, but in this one case, it generates a forecast that doesn’t jibe with common sense.

If you’re interested in the rankings behind these forecasts, click here; for more background on the system, here.

Women’s odds were posted earlier today, and both forecasts will be updated throughout the tournament–I’ll post those links when I have them, probably mid-day Tuesday.

    Player                   R64    R32    R16        W  
1   Novak Djokovic         96.8%  81.3%  70.3%    26.2%  
    Juan Carlos Ferrero     3.2%   0.5%   0.1%     0.0%  
    Ryan Harrison          55.0%  10.7%   6.1%     0.3%  
    Yen-Hsun Lu            45.0%   7.6%   3.9%     0.1%  
    Benjamin Becker        53.2%  25.2%   4.8%     0.1%  
    James Blake            46.8%  20.5%   3.6%     0.0%  
    Sergiy Stakhovsky      54.4%  30.6%   6.7%     0.2%  
28  Radek Stepanek         45.6%  23.8%   4.7%     0.1%  

    Player                   R64    R32    R16        W  
24  Marcel Granollers      53.7%  42.9%  26.4%     1.0%  
    Viktor Troicki         46.3%  35.9%  20.9%     0.6%  
    Martin Klizan          65.0%  15.9%   5.4%     0.0%  
    Juan Ignacio Chela     35.0%   5.3%   1.1%     0.0%  
    Jeremy Chardy          88.5%  48.2%  22.9%     0.4%  
    Filippo Volandri       11.5%   1.8%   0.2%     0.0%  
    Leonardo Mayer         18.7%   4.7%   0.9%     0.0%  
15  Juan Monaco            81.3%  45.3%  22.1%     0.4%  

    Player                   R64    R32    R16        W  
12  Nicolas Almagro        60.2%  36.6%  18.6%     0.3%  
    Olivier Rochus         39.8%  20.3%   8.2%     0.0%  
    Guillaume Rufin        21.0%   4.4%   0.8%     0.0%  
    Steve Darcis           79.0%  38.8%  17.8%     0.2%  
    Carlos Berlocq         18.7%   2.6%   0.5%     0.0%  
    Ruben Bemelmans        81.3%  33.7%  16.7%     0.2%  
    Tobias Kamke           37.3%  21.0%  10.5%     0.1%  
18  Richard Gasquet        62.7%  42.7%  26.8%     1.0%  

    Player                   R64    R32    R16        W  
31  Florian Mayer          60.7%  38.2%  19.4%     0.8%  
    Dmitry Tursunov        39.3%  20.8%   8.3%     0.1%  
    Philipp Petzschner     54.8%  23.6%   9.6%     0.2%  
    Blaz Kavcic            45.2%  17.4%   6.2%     0.1%  
    Simone Bolelli         51.0%  17.4%   8.0%     0.1%  
    Jerzy Janowicz         49.0%  16.3%   7.2%     0.1%  
    Ernests Gulbis         36.3%  21.1%  11.4%     0.4%  
6   Tomas Berdych          63.7%  45.3%  29.9%     2.6%  

    Player                   R64    R32    R16        W  
3   Roger Federer          92.0%  73.7%  59.4%    10.4%  
    Albert Ramos            8.0%   2.1%   0.6%     0.0%  
    Fabio Fognini          36.9%   6.9%   3.0%     0.0%  
    Michael Llodra         63.1%  17.3%   9.6%     0.2%  
    A Menendez-Maceiras    31.5%   6.6%   0.8%     0.0%  
    Michael Russell        68.5%  25.3%   5.5%     0.0%  
    Gilles Muller          43.3%  28.2%   7.9%     0.1%  
29  Julien Benneteau       56.7%  39.9%  13.4%     0.3%  

    Player                   R64    R32    R16        W  
17  Fernando Verdasco      88.2%  53.1%  27.7%     0.8%  
    Jimmy Wang             11.8%   2.4%   0.3%     0.0%  
    Grega Zemlja           90.7%  43.6%  20.1%     0.3%  
    Josh Goodall            9.3%   0.9%   0.1%     0.0%  
    Xavier Malisse         51.6%  21.7%   9.8%     0.1%  
    Marinko Matosevic      48.4%  19.7%   8.6%     0.1%  
    Paul-Henri Mathieu     12.6%   2.5%   0.5%     0.0%  
13  Gilles Simon           87.4%  56.1%  32.8%     1.4%  

    Player                   R64    R32    R16        W  
11  John Isner             66.6%  46.0%  28.9%     1.2%  
    Alejandro Falla        33.4%  17.7%   8.2%     0.1%  
    Paolo Lorenzi          21.2%   3.4%   0.7%     0.0%  
    Nicolas Mahut          78.8%  32.9%  16.2%     0.2%  
    Igor Andreev           87.6%  37.3%  15.6%     0.1%  
    Oliver Golding         12.4%   1.2%   0.1%     0.0%  
    Denis Istomin          47.2%  28.3%  13.5%     0.2%  
23  Andreas Seppi          52.8%  33.2%  16.9%     0.4%  

    Player                   R64    R32    R16        W  
26  Mikhail Youzhny        50.8%  33.2%  16.3%     0.4%  
    Donald Young           49.2%  31.9%  15.3%     0.4%  
    Inigo Cervantes        20.2%   2.9%   0.4%     0.0%  
    Flavio Cipolla         79.8%  32.0%  12.5%     0.2%  
    Ryan Sweeting          79.1%  29.1%  13.8%     0.2%  
    Potito Starace         20.9%   2.8%   0.5%     0.0%  
    David Nalbandian       49.6%  33.7%  20.3%     0.9%  
8   Janko Tipsarevic       50.4%  34.4%  20.9%     1.0%  

    Player                   R64    R32    R16        W  
7   David Ferrer           70.4%  49.1%  32.6%     1.7%  
    Dustin Brown           29.6%  14.6%   6.7%     0.1%  
    Kenny De Schepper      40.5%  12.6%   5.3%     0.0%  
    Matthias Bachinger     59.5%  23.6%  11.8%     0.1%  
    Wayne Odesnik          30.0%   5.6%   1.0%     0.0%  
    Bjorn Phau             70.0%  24.3%   8.0%     0.0%  
    Jamie Baker            36.0%  22.5%   9.0%     0.1%  
30  Andy Roddick           64.0%  47.7%  25.5%     0.7%  

    Player                   R64    R32    R16        W  
19  Kei Nishikori          64.7%  52.4%  30.5%     1.9%  
    Mikhail Kukushkin      35.3%  24.7%  11.0%     0.2%  
    Andrey Kuznetsov       33.3%   5.1%   0.9%     0.0%  
    Florent Serra          66.7%  17.8%   5.3%     0.0%  
    Go Soeda               52.2%  16.7%   6.7%     0.0%  
    Igor Kunitsyn          47.8%  14.5%   5.4%     0.0%  
    Robin Haase            30.0%  16.5%   7.3%     0.1%  
9   J Del Potro            70.0%  52.3%  32.8%     2.4%  

    Player                   R64    R32    R16        W  
16  Marin Cilic            61.8%  42.7%  25.1%     1.5%  
    Cedrik-Marcel Stebe    38.2%  22.0%  10.3%     0.2%  
    Tatsuma Ito            50.0%  17.7%   6.8%     0.1%  
    Lukasz Kubot           50.0%  17.6%   6.9%     0.1%  
    Vasek Pospisil         38.9%  17.0%   7.5%     0.1%  
    Sam Querrey            61.1%  33.7%  18.5%     0.8%  
    Santiago Giraldo       42.8%  19.4%   8.9%     0.2%  
21  Milos Raonic           57.2%  30.0%  16.0%     0.5%  

    Player                   R64    R32    R16        W  
32  Kevin Anderson         53.2%  29.8%  12.7%     0.5%  
    Grigor Dimitrov        46.8%  24.9%   9.9%     0.3%  
    Albert Montanes        20.8%   4.5%   0.8%     0.0%  
    Marcos Baghdatis       79.2%  40.9%  17.6%     0.7%  
    Ivo Karlovic           39.3%   8.1%   2.5%     0.0%  
    Dudi Sela              60.7%  17.3%   7.1%     0.1%  
    Nikolay Davydenko      24.1%  13.7%   6.0%     0.1%  
4   Andy Murray            75.9%  61.0%  43.3%     7.0%  

    Player                   R64    R32    R16        W  
5   Jo-Wilfried Tsonga     93.2%  69.4%  47.0%     5.2%  
    Lleyton Hewitt          6.8%   1.2%   0.2%     0.0%  
    E Roger-Vasselin       44.5%  12.0%   4.7%     0.1%  
    G Garcia-Lopez         55.5%  17.4%   7.8%     0.1%  
    Lukas Lacko            82.2%  34.6%  12.5%     0.3%  
    Adrian Ungur           17.8%   2.4%   0.3%     0.0%  
    Jurgen Melzer          35.8%  19.6%   6.8%     0.1%  
25  Stanislas Wawrinka     64.2%  43.3%  20.8%     1.1%  

    Player                   R64    R32    R16        W  
20  Bernard Tomic          78.7%  51.6%  27.4%     1.0%  
    David Goffin           21.4%   7.3%   1.8%     0.0%  
    Jesse Levine           56.2%  24.4%  10.0%     0.1%  
    Karol Beck             43.8%  16.7%   5.9%     0.0%  
    James Ward             76.3%  28.0%  12.6%     0.1%  
    Pablo Andujar          23.7%   4.0%   0.9%     0.0%  
    R Ramirez Hidalgo       6.7%   1.1%   0.1%     0.0%  
10  Mardy Fish             93.3%  66.9%  41.3%     2.2%  

    Player                   R64    R32    R16        W  
14  Feliciano Lopez        58.5%  52.2%  28.3%     0.7%  
    Jarkko Nieminen        41.5%  35.4%  16.0%     0.2%  
    Brian Baker            33.8%   2.7%   0.2%     0.0%  
    Rui Machado            66.2%   9.8%   1.5%     0.0%  
    Matthew Ebden          58.7%  25.7%  13.2%     0.1%  
    Benoit Paire           41.3%  14.5%   6.3%     0.0%  
    Alex Bogomolov Jr.     39.8%  21.4%  11.0%     0.1%  
22  Alexandr Dolgopolov    60.2%  38.4%  23.4%     0.7%  

    Player                   R64    R32    R16        W  
27  Philipp Kohlschreiber  81.1%  52.3%  19.6%     0.9%  
    Tommy Haas             18.9%   5.9%   0.8%     0.0%  
    Jurgen Zopp            74.2%  35.2%  10.4%     0.2%  
    Malek Jaziri           25.8%   6.6%   0.9%     0.0%  
    Lukas Rosol            39.9%   9.2%   4.1%     0.1%  
    Ivan Dodig             60.1%  18.3%  10.0%     0.4%  
    Thomaz Bellucci        17.2%   7.5%   3.4%     0.1%  
2   Rafael Nadal           82.8%  64.9%  50.8%    12.0%

2012 Wimbledon Women’s Projections

Here are my forecasts for the Wimbledon women’s draw.  Despite Maria Sharapova’s performance at the French, my ranking system still has her third, behind both Serena and Azarenka.  Also, you might also be surprised by the significant chance I give Kim Clijsters.  While she hasn’t played much, she’s played well, and my system operates on the assumption that if someone takes the court, she is doing so fully healthy.  (Or, at least, as healthy as she’s been other times she took the court.)

If you’re interested in the rankings behind these forecasts, click here; for more background on the system, here.

I’ll post men’s odds later today, and the forecast will be updated throughout the tournament–I’ll post those links when I have them, probably mid-day Tuesday.

    Player                   R64    R32    R16        W  
1   Maria Sharapova        89.1%  68.7%  56.4%     8.7%  
    Anastasia Rodionova    10.9%   3.4%   1.2%     0.0%  
    Vesna Dolonc           21.8%   2.8%   0.9%     0.0%  
    Tsvetana Pironkova     78.2%  25.1%  15.9%     0.4%  
    Su-Wei Hsieh           51.5%  29.5%   8.0%     0.1%  
    Virginie Razzano       48.5%  27.0%   7.1%     0.1%  
    S Foretz Gacon         28.2%   7.8%   1.0%     0.0%  
29  Monica Niculescu       71.8%  35.6%   9.5%     0.1%  

    Player                   R64    R32    R16        W  
23  Petra Cetkovska        55.0%  38.3%  19.9%     0.3%  
    Vania King             45.0%  29.3%  13.8%     0.1%  
    Sloane Stephens        63.2%  23.1%   8.6%     0.0%  
    Karolina Pliskova      36.8%   9.2%   2.4%     0.0%  
    Bojana Jovanovski      58.0%  17.0%   6.8%     0.0%  
    Eleni Daniilidou       42.0%   9.9%   3.2%     0.0%  
    Petra Martic           32.6%  20.5%  10.2%     0.1%  
15  Sabine Lisicki         67.4%  52.6%  35.2%     1.2%  

    Player                   R64    R32    R16        W  
12  Vera Zvonareva         75.3%  64.2%  33.5%     2.3%  
    Mona Barthel           24.8%  16.3%   4.7%     0.0%  
    Edina Gallovits-Hall   37.5%   5.6%   0.8%     0.0%  
    Silvia Soler-Espinosa  62.5%  13.8%   3.0%     0.0%  
    Kai-Chen Chang         52.0%   7.6%   2.0%     0.0%  
    Andrea Hlavackova      48.0%   6.5%   1.5%     0.0%  
    Kim Clijsters          70.6%  63.1%  43.1%     5.4%  
18  Jelena Jankovic        29.4%  22.9%  11.4%     0.3%  

    Player                   R64    R32    R16        W  
28  Christina McHale       79.2%  64.0%  30.0%     0.7%  
    Johanna Konta          20.8%  11.0%   2.1%     0.0%  
    Lesia Tsurenko         59.4%  16.4%   3.4%     0.0%  
    Mathilde Johansson     40.6%   8.7%   1.3%     0.0%  
    Ekaterina Makarova     80.8%  38.2%  23.5%     0.6%  
    Alberta Brianti        19.2%   3.7%   1.1%     0.0%  
    Lucie Hradecka         19.1%   6.1%   2.3%     0.0%  
8   Angelique Kerber       80.9%  52.1%  36.1%     2.1%  

    Player                   R64    R32    R16        W  
3   Agnieszka Radwanska    88.4%  70.6%  50.7%     7.3%  
    Magdalena Rybarikova   11.6%   4.0%   1.1%     0.0%  
    Venus Williams         51.5%  13.3%   5.4%     0.1%  
    Elena Vesnina          48.5%  12.1%   4.8%     0.0%  
    Iveta Benesova         72.8%  35.1%  13.0%     0.3%  
    Heather Watson         27.2%   7.5%   1.4%     0.0%  
    Jamie Lee Hampton      20.8%   6.9%   1.3%     0.0%  
27  Daniela Hantuchova     79.2%  50.6%  22.3%     1.0%  

    Player                   R64    R32    R16        W  
20  Nadia Petrova          83.0%  56.0%  28.4%     0.4%  
    Maria Elena Camerin    17.0%   5.1%   1.0%     0.0%  
    Timea Babos            40.1%  13.3%   4.1%     0.0%  
    Melanie Oudin          59.9%  25.6%   9.9%     0.0%  
    Tamarine Tanasugarn    52.2%  11.9%   3.8%     0.0%  
    Anna Tatishvili        47.8%  10.1%   3.0%     0.0%  
    Camila Giorgi          18.9%  10.2%   3.6%     0.0%  
16  Flavia Pennetta        81.1%  67.9%  46.1%     1.7%  

    Player                   R64    R32    R16        W  
11  Na Li                  77.1%  60.0%  44.4%     3.8%  
    Ksenia Pervak          22.9%  11.8%   5.7%     0.0%  
    Sorana Cirstea         69.4%  22.5%  11.9%     0.1%  
    Pauline Parmentier     30.6%   5.6%   2.0%     0.0%  
    Naomi Broady           43.8%  10.4%   1.7%     0.0%  
    L Dominguez Lino       56.2%  16.1%   3.4%     0.0%  
    Alexandra Cadantu      15.1%   6.1%   0.9%     0.0%  
17  Maria Kirilenko        84.9%  67.4%  29.9%     0.6%  

    Player                   R64    R32    R16        W  
30  Shuai Peng             80.2%  54.1%  23.5%     0.4%  
    Sandra Zaniewska       19.8%   6.8%   1.3%     0.0%  
    Jarmila Gajdosova      59.4%  25.3%   7.9%     0.0%  
    Ayumi Morita           40.6%  13.9%   3.4%     0.0%  
    Arantxa Rus            52.1%  10.3%   3.7%     0.0%  
    Misaki Doi             47.9%   9.1%   3.1%     0.0%  
    Carla Suarez Navarro   17.6%   9.9%   4.0%     0.0%  
5   Samantha Stosur        82.4%  70.7%  53.1%     4.2%  

    Player                   R64    R32    R16        W  
6   Serena Williams        90.4%  80.9%  67.2%    16.1%  
    B Zahlavova Strycova    9.6%   4.6%   1.7%     0.0%  
    Johanna Larsson        43.5%   5.7%   2.0%     0.0%  
    Melinda Czink          56.5%   8.8%   3.6%     0.0%  
    Vera Dushevina         47.7%  19.1%   3.9%     0.0%  
    Aleksandra Wozniak     52.3%  22.2%   4.7%     0.0%  
    Stephanie Dubois       18.9%   5.7%   0.7%     0.0%  
25  Jie Zheng              81.1%  53.0%  16.2%     0.4%  

    Player                   R64    R32    R16        W  
19  Lucie Safarova         80.1%  57.6%  39.9%     0.8%  
    Kiki Bertens           19.9%   7.7%   2.7%     0.0%  
    Chanelle Scheepers     49.7%  17.2%   7.9%     0.0%  
    Yaroslava Shvedova     50.3%  17.6%   8.3%     0.0%  
    Laura Pous-Tio         35.9%   9.4%   2.2%     0.0%  
    Anne Keothavong        64.1%  24.9%   8.7%     0.0%  
    Coco Vandeweghe        33.8%  18.7%   6.6%     0.0%  
10  Sara Errani            66.2%  47.0%  23.7%     0.2%  

    Player                   R64    R32    R16        W  
13  Dominika Cibulkova     64.4%  53.7%  38.7%     1.5%  
    Klara Zakopalova       35.6%  26.2%  15.6%     0.1%  
    Olga Govortsova        50.6%  10.2%   3.6%     0.0%  
    Annika Beck            49.4%   9.9%   3.5%     0.0%  
    Polona Hercog          64.5%  28.1%  10.0%     0.0%  
    Kristyna Pliskova      35.5%  10.8%   2.7%     0.0%  
    Laura Robson           31.3%  15.2%   4.6%     0.0%  
24  Francesca Schiavone    68.7%  45.9%  21.3%     0.2%  

    Player                   R64    R32    R16        W  
31  A Pavlyuchenkova       64.0%  50.0%  18.4%     0.5%  
    Sofia Arvidsson        36.0%  24.0%   6.4%     0.0%  
    P Mayr-Achleitner      34.2%   6.2%   0.8%     0.0%  
    Varvara Lepchenko      65.8%  19.8%   3.9%     0.0%  
    Elena Baltacha         64.5%  10.0%   3.5%     0.0%  
    Karin Knapp            35.5%   3.5%   0.8%     0.0%  
    A Amanmuradova          9.4%   4.7%   1.4%     0.0%  
4   Petra Kvitova          90.6%  81.7%  64.8%     9.0%  

    Player                   R64    R32    R16        W  
7   Caroline Wozniacki     82.7%  71.2%  50.1%     5.0%  
    Tamira Paszek          17.3%   9.7%   3.3%     0.0%  
    Alize Cornet           55.1%  11.2%   3.3%     0.0%  
    Nina Bratchikova       44.9%   7.9%   2.0%     0.0%  
    Greta Arn              34.0%   9.8%   2.6%     0.0%  
    Galina Voskoboeva      66.0%  29.3%  11.6%     0.2%  
    Yanina Wickmayer       50.0%  30.4%  13.5%     0.3%  
32  Svetlana Kuznetsova    50.0%  30.5%  13.6%     0.3%  

    Player                   R64    R32    R16        W  
21  Roberta Vinci          81.4%  50.9%  22.1%     0.2%  
    Ashleigh Barty         18.6%   5.2%   0.9%     0.0%  
    Urszula Radwanska      48.3%  21.1%   7.0%     0.0%  
    Marina Erakovic        51.7%  22.9%   7.7%     0.0%  
    Mirjana Lucic          49.0%   8.2%   2.4%     0.0%  
    Alexandra Panova       51.0%   8.8%   2.6%     0.0%  
    Casey Dellacqua        18.1%  11.1%   4.4%     0.0%  
9   Marion Bartoli         81.9%  71.9%  53.0%     2.9%  

    Player                   R64    R32    R16        W  
14  Ana Ivanovic           67.6%  51.4%  34.2%     1.3%  
    M Martinez Sanchez     32.4%  19.7%   9.8%     0.1%  
    Kimiko Date-Krumm      31.7%   6.1%   1.8%     0.0%  
    Kateryna Bondarenko    68.3%  22.9%  10.5%     0.0%  
    Anastasiya Yakimova    49.0%  10.2%   2.2%     0.0%  
    Mandy Minella          51.0%  11.2%   2.5%     0.0%  
    Shahar Peer            37.1%  26.9%  11.1%     0.1%  
22  Julia Goerges          62.9%  51.7%  27.8%     0.6%  

    Player                   R64    R32    R16        W  
26  A Medina Garrigues     40.2%  27.6%   5.7%     0.0%  
    Simona Halep           59.8%  46.3%  12.4%     0.2%  
    Jana Cepelova          45.5%  11.2%   1.2%     0.0%  
    Kristina Mladenovic    54.5%  15.0%   1.8%     0.0%  
    Irina-Camelia Begu     34.9%   3.2%   1.0%     0.0%  
    Romina Oprandi         65.1%   9.6%   4.4%     0.0%  
    Irina Falconi           7.6%   3.4%   1.3%     0.0%  
2   Victoria Azarenka      92.4%  83.8%  72.1%    17.0%

TennisAbstract.com, Supersized

I’m proud to announce the new, improved TennisAbstract.com, now with challengers, qualifiers, Davis Cup, and ATP matches back to 1968.

Previously, the site was limited to ATP-level matches back to 1991.  Now, the number of matches available has increased from 70,000 to 240,000, and the number of players with a page on the site has jumped from roughly 1,600 to almost 7,500.

Historical Matches

TA now includes every tour-level match ever played.  (In theory, anyway.)  You can check out Arthur Ashe’s career record, or his head-to-head with Rod Laver, or his performance in finals.  This dataset goes back to 1968.

Challengers

The biggest addition to the site is at the challenger level.  I’ve added nearly 90,000 challenger matches, which include all main-draw results since 1991.  Stats, including ace percentage and first serve percentage, are available for most challenger matches of the last five years.  For instance, see the epic 2011 Challenger season of Cedrik-Marcel Stebe.

Qualifying Rounds

Many players split their time between challengers and qualifiers, so it wouldn’t make sense to have one without the other.  Qualifying matches for tour-level events back to 2007 are now in the database, and most have stats.   A glance at David Goffin’s page now tells the more complete story of his path to the French Open round of 16.

Davis Cup

Since I launched the site, Davis Cup has been the most frequent request.  Now it’s here.  World Group back to 1981.  World Group play-offs back to 2003.  Groups I and II back to 1994.  You can now check out the Davis Cup career of Andy Roddick, among hundreds of others.

I hope you enjoy these additions to TennisAbstract.  As always, please let me know if you find bugs or errors, or if you have suggestions to improve the site.

Men’s and Women’s French Open Forecasts, Updating Live

Every match completed at Roland Garros has implications on the title chances of several players.  I’ve created two pages that update throughout the tournament to track each player’s odds of reaching each successive round:

For reference, you can check each player’s pre-tournament odds: men and women.

The Official JRank Reference

Italian translation at settesei.it

At HeavyTopspin, I frequently post references to “my rankings” which power my tournament projections.  (For instance, 2012 French Open men and women.)  My system is unofficially called “JRank”–in other words, it needs a new name.    The rankings it generates are superior to the ATP (and presumably WTA) rankings in the sense that they better predict the outcome of tour- and challenger-level matches.

The algorithm is complex but the ideas behind it are not.  The fundamental difference between JRank and the ATP system is how it values individual matches.

The ATP system awards points based on tournament and round.  (A first round win at Wimbledon is worth more than a first round win at Halle; a third round win at Roland Garros is worth more than a second round win.)  JRank, by contrast, awards points based on opponent and recency.  In my system, a win against Rafael Nadal is worth much more than a defeat of Igor Kunitsyn, even if both take place in the same round at the same tournament.  And a defeat of Kunitsyn is worth more if it took place last week than if it took place eight months ago.  A recent win tells you more about a player’s current ability level than an older one does.

The advantage of giving recent matches more weight is that it allows us to take into account matches more than one year old, without the veteran-favoring disadvantages of Nadal’s two-year plan.  JRank uses all matches from the last two years, but a match one year ago is worth only half as much as a match last week, while a match two years ago is worth only a quarter as much.  That way, we get the benefits of that much more data, but without unduly favoring vets.  There is the added benefit that JRank is “smoother” from week to week–none of the bizarre effects of a tournament “falling off” from last year–as if a player’s results 51 weeks ago are 100% more relevant than his results 54 weeks ago!

JRank’s value is even greater because it generates separate rankings for clay and hard surfaces.  Everyone knows that surface matters, but the ATP ranking system ignores it completely.  If you want to know who should be favored at the French, it seems silly to weight Bercy as heavily as Monte Carlo.  JRank gives more weight to a player’s clay record for his clay ranking, and so on.  Even further, beating a clay court specialist is worth more on clay than it is on a hard court.

Creating projections

Armed with rankings, it’s a few small steps to generating a forecast for any tournament.  For each match, the projection is based almost entirely on the rankings of the two players.  (The formula is a slightly more complicated version of A divided by A+B, where A is one player’s ranking point and B is the other’s.  It works–approximately–with ATP ranking points as well.)

There are a few tweaks, though.  First, my research has indicated that qualifiers, lucky losers, and wild cards all perform slightly below expectations.  It is unclear why, though with qualifiers I suspect it is due to fatigue–while their opponents rested, they played two or three tough matches to qualify.

Second, I’ve established that there is a slight home court advantage.  When surface is accounted for, home court advantage is minimal, but it is still there–the “home” player performs about 2% better than expected.  Perhaps it’s referee bias, home cooking, fan support, or some combination of the above.

A frequent suggestion is to incorporate head-to-head records into match projections.  It’s a tempting idea–so tempting that I’ve tried it.  However, it doesn’t seem to make much difference, at least for any broad cross-section of matches.  (Perhaps when a pair of players have, say, 10 or more head-to-head matches in the books, stronger patterns emerge.)  For the most part, it seems that if a ranking system represents a good approximation of each player’s ability level, head-to-head results are superfluous.

There may be other variables worth looking at, including the importance of the tournament, the player’s fatigue level or recent injury history, or each player’s experience at a particular event.  For now, those are among the influences I haven’t even tested.

2012 French Open Women’s Projections

For the Grand Slams, my ranking system takes aim at the WTA, too.  Here are pre-tournament odds for each player in the draw.

(Yes, it’s mid-day Monday and many first round matches are in the books.  I’ll post a link with automatically-updating odds soon; pre-tournament numbers on the record for comparison’s sake.)

    Player                      R64    R32    R16        W  
1   Victoria Azarenka         91.6%  85.8%  73.9%    14.3%  
    Alberta Brianti            8.4%   4.8%   1.8%     0.0%  
    Caroline Garcia           55.3%   5.6%   1.8%     0.0%  
    Dinah Pfizenmaier         44.7%   3.9%   1.1%     0.0%  
    Heidi El Tabakh           29.5%   9.0%   1.1%     0.0%  
    Aleksandra Wozniak        70.5%  36.2%   8.2%     0.1%  
    Alize Cornet              40.1%  19.5%   3.6%     0.0%  
31  Jie Zheng                 59.9%  35.3%   8.5%     0.1%  
                                                            
    Player                      R64    R32    R16        W  
20  Lucie Safarova            82.9%  57.2%  25.9%     0.5%  
    Anastasiya Yakimova       17.1%   5.6%   0.9%     0.0%  
    MJ Martinez Sanchez       74.6%  31.6%  10.5%     0.0%  
    Eva Birnerova             25.4%   5.6%   0.9%     0.0%  
    Vania King                57.6%  20.6%  10.7%     0.1%  
    Galina Voskoboeva         42.4%  12.5%   5.6%     0.0%  
    Kristina Mladenovic       12.7%   3.6%   1.0%     0.0%  
15  Dominika Cibulkova        87.3%  63.3%  44.5%     2.6%  
                                                            
    Player                      R64    R32    R16        W  
12  Sabine Lisicki            65.9%  35.2%  23.0%     0.5%  
    Bethanie Mattek-Sands     34.1%  12.7%   6.2%     0.0%  
    Ekaterina Makarova        69.5%  40.4%  27.3%     0.8%  
    Sloane Stephens           30.5%  11.7%   5.7%     0.0%  
    Mathilde Johansson        40.8%  10.9%   2.4%     0.0%  
    Anastasia Rodionova       59.2%  20.8%   6.2%     0.0%  
    Simona Halep              53.2%  37.1%  16.5%     0.2%  
24  Petra Cetkovska           46.8%  31.1%  12.7%     0.1%  
                                                            
    Player                      R64    R32    R16        W  
27  Nadia Petrova             55.7%  37.4%  15.3%     0.2%  
    Iveta Benesova            44.3%  27.4%   9.9%     0.1%  
    Laura Pous-Tio            37.5%  10.6%   2.4%     0.0%  
    Chanelle Scheepers        62.5%  24.7%   7.8%     0.0%  
    Irina Falconi             48.9%   8.4%   2.6%     0.0%  
    Edina Gallovits-Hall      51.1%   9.1%   2.9%     0.0%  
    Elena Baltacha            15.6%   8.8%   3.3%     0.0%  
6   Samantha Stosur           84.4%  73.7%  55.9%     4.4%  
                                                            
    Player                      R64    R32    R16        W  
3   Agnieszka Radwanska       86.1%  62.3%  47.5%     4.7%  
    Bojana Jovanovski         13.9%   4.4%   1.6%     0.0%  
    Venus Williams            78.7%  29.9%  18.4%     0.4%  
    Paula Ormaechea           21.3%   3.4%   1.1%     0.0%  
    Yung-Jan Chan             34.1%   8.6%   1.3%     0.0%  
    Kateryna Bondarenko       65.9%  25.3%   6.1%     0.0%  
    Mirjana Lucic             22.1%   9.6%   1.6%     0.0%  
26  Svetlana Kuznetsova       77.9%  56.5%  22.5%     0.5%  
                                                            
    Player                      R64    R32    R16        W  
21  Sara Errani               70.2%  48.9%  21.5%     0.3%  
    Casey Dellacqua           29.8%  14.8%   3.9%     0.0%  
    Melanie Oudin             40.7%  12.7%   3.0%     0.0%  
    Johanna Larsson           59.3%  23.6%   7.1%     0.0%  
    Stephanie Dubois          24.1%   4.3%   1.3%     0.0%  
    Shahar Peer               75.9%  28.7%  16.1%     0.1%  
    L Arruabarrena-Vecino     13.3%   4.1%   1.3%     0.0%  
13  Ana Ivanovic              86.7%  63.0%  45.9%     2.2%  
                                                            
    Player                      R64    R32    R16        W  
10  Angelique Kerber          88.3%  73.8%  56.2%     4.3%  
    Shuai Zhang               11.7%   4.7%   1.5%     0.0%  
    Romina Oprandi            46.5%   9.5%   3.7%     0.0%  
    Olga Govortsova           53.5%  11.9%   4.9%     0.0%  
    Anna Tatishvili           58.0%  18.0%   4.1%     0.0%  
    Alexa Glatch              42.0%  10.5%   1.9%     0.0%  
    Su-Wei Hsieh              31.8%  19.2%   5.3%     0.0%  
18  Flavia Pennetta           68.2%  52.2%  22.3%     0.4%  
                                                            
    Player                      R64    R32    R16        W  
29  A. Medina Garrigues       66.8%  48.5%  20.5%     0.1%  
    Laura Robson              33.2%  19.1%   5.4%     0.0%  
    Kai-Chen Chang            50.4%  16.4%   3.8%     0.0%  
    Irena Pavlovic            49.7%  16.0%   3.6%     0.0%  
    Petra Martic              58.2%  17.2%   8.9%     0.0%  
    Michaella Krajicek        41.8%   9.7%   4.3%     0.0%  
    Karolina Pliskova         15.4%   6.4%   2.5%     0.0%  
8   Marion Bartoli            84.6%  66.7%  51.1%     1.7%  
                                                            
    Player                      R64    R32    R16        W  
7   Na Li                     78.4%  71.0%  57.8%     8.4%  
    Sorana Cirstea            21.6%  15.6%   8.5%     0.1%  
    B Zahlavova Strycova      59.4%   8.9%   3.2%     0.0%  
    S Foretz Gacon            40.6%   4.5%   1.2%     0.0%  
    Christina McHale          75.3%  45.7%  15.4%     0.2%  
    Kiki Bertens              24.7%   8.5%   1.4%     0.0%  
    Lauren Davis              35.5%  13.0%   2.7%     0.0%  
30  Mona Barthel              64.5%  32.7%   9.7%     0.1%  
                                                            
    Player                      R64    R32    R16        W  
17  Roberta Vinci             50.3%  34.3%  22.7%     0.2%  
    Sofia Arvidsson           49.7%  33.7%  22.4%     0.2%  
    Yaroslava Shvedova        60.0%  21.3%  11.1%     0.0%  
    Mandy Minella             40.0%  10.7%   4.6%     0.0%  
    Tamarine Tanasugarn       25.3%   9.9%   2.4%     0.0%  
    Carla Suarez Navarro      74.7%  48.8%  23.3%     0.1%  
    Timea Babos               52.4%  22.3%   7.6%     0.0%  
    Sesil Karatantcheva       47.6%  19.0%   5.9%     0.0%  
                                                            
    Player                      R64    R32    R16        W  
14  Francesca Schiavone       81.6%  42.3%  25.8%     0.3%  
    Kimiko Date-Krumm         18.4%   3.7%   1.0%     0.0%  
    Tsvetana Pironkova        38.2%  18.1%   9.4%     0.1%  
    Yanina Wickmayer          61.8%  35.8%  22.5%     0.4%  
    Varvara Lepchenko         54.7%  23.0%   8.4%     0.0%  
    Ksenia Pervak             45.3%  17.3%   5.6%     0.0%  
    P Mayr-Achleitner         24.2%   9.4%   2.3%     0.0%  
19  Jelena Jankovic           75.8%  50.2%  25.1%     0.3%  
                                                            
    Player                      R64    R32    R16        W  
32  Monica Niculescu          64.8%  37.8%   8.0%     0.0%  
    Nina Bratchikova          35.2%  15.4%   2.1%     0.0%  
    Vera Dushevina            62.2%  31.9%   6.2%     0.0%  
    Claire Feuerstein         37.8%  14.9%   2.0%     0.0%  
    Pauline Parmentier        43.5%   6.1%   3.0%     0.0%  
    Urszula Radwanska         56.5%   9.7%   5.4%     0.0%  
    Ashleigh Barty             4.5%   1.1%   0.3%     0.0%  
4   Petra Kvitova             95.5%  83.0%  73.1%     8.5%  
                                                            
    Player                      R64    R32    R16        W  
5   Serena Williams           93.2%  87.6%  74.0%    23.3%  
    Virginie Razzano           6.8%   3.6%   1.1%     0.0%  
    Arantxa Rus               56.2%   5.4%   1.6%     0.0%  
    Jamie Hampton             43.8%   3.5%   0.9%     0.0%  
    Elena Vesnina             70.8%  29.7%   5.8%     0.1%  
    Heather Watson            29.2%   6.9%   0.7%     0.0%  
    Lucie Hradecka            32.2%  16.6%   2.9%     0.0%  
25  Julia Goerges             67.8%  46.7%  13.0%     0.6%  
                                                            
    Player                      R64    R32    R16        W  
23  Kaia Kanepi               75.3%  53.4%  22.4%     0.2%  
    Alexandra Panova          24.7%  11.0%   2.3%     0.0%  
    Irina-Camelia Begu        51.8%  18.8%   4.7%     0.0%  
    Aravane Rezai             48.2%  16.8%   3.9%     0.0%  
    Jarmila Gajdosova         56.8%  18.8%  10.2%     0.0%  
    Magdalena Rybarikova      43.2%  12.0%   5.8%     0.0%  
    Eleni Daniilidou          11.3%   3.1%   0.9%     0.0%  
9   Caroline Wozniacki        88.7%  66.1%  49.8%     2.3%  
                                                            
    Player                      R64    R32    R16        W  
16  Maria Kirilenko           75.9%  48.1%  24.8%     0.1%  
    Victoria Larriere         24.1%   8.7%   2.5%     0.0%  
    Klara Zakopalova          64.8%  31.2%  13.6%     0.0%  
    Lesia Tsurenko            35.2%  12.0%   3.8%     0.0%  
    Anne Keothavong           42.6%   9.6%   3.0%     0.0%  
    Melinda Czink             57.4%  15.7%   5.8%     0.0%  
    Greta Arn                 26.3%  15.5%   6.7%     0.0%  
22  Anastasia Pavlyuchenkova  73.7%  59.2%  39.9%     0.6%  
                                                            
    Player                      R64    R32    R16        W  
28  Shuai Peng                67.8%  44.7%  10.8%     0.1%  
    Tamira Paszek             32.2%  15.7%   2.3%     0.0%  
    Marina Erakovic           63.2%  28.0%   4.9%     0.0%  
    Lourdes Dominguez Lino    36.8%  11.6%   1.3%     0.0%  
    Polona Hercog             61.7%  11.2%   6.2%     0.0%  
    Ayumi Morita              38.3%   4.9%   2.2%     0.0%  
    Alexandra Cadantu          6.3%   2.1%   0.7%     0.0%  
2   Maria Sharapova           93.7%  81.9%  71.5%    14.8%

2012 French Open Projections

Yesterday we saw who gained and lost from the French Open draw.  Today we get to what you really care about: Each player’s odds of progressing through the tournament.

According to my ranking system, combined with the actual draw, this year’s favorite is … a tie.  How’s that for a cop out–virtually even odds for Rafael Nadal and Novak Djokovic, both with roughly 30% chances of winning the event.  Roger Federer is in a distant third at 12%, with the unlikely Janko Tipsarevic far behind him in fourth with 5.7%.  No one (including myself) cares much for Janko’s chances, but this is a man who has beaten both Djokovic and Tomas Berdych on clay.  With the exception of David Ferrer (languishing as 8th favorite, below 3%), no one in the following pack has shown much clay-court consistency.

The highest-rated non-seeds are David Nalbandian, Thomaz Bellucci, and Marcos Baghdatis.  Nalbandian, of course, has a probable second-rounder with Federer, but if he gets through it, he’ll have the benefits of Federer’s easy early-round draw.  Baghdatis will have an early test in Nicolas Almagro, a man who is in form but may have spent his energy in the wrong French city.  And Bellucci drew Viktor Troicki, one of the weakest seeds, despite the Serb’s strong showing in Dusseldorf this week.

The full odds are below.  By Tuesday or Wednesday, I should have a page published that will update odds throughout the tournament.

    Player                    R64    R32    R16        W  
1   Novak Djokovic          96.6%  93.4%  88.1%    30.2%  
    Potito Starace           3.4%   1.8%   0.7%     0.0%  
    Blaz Kavcic             78.1%   4.5%   2.0%     0.0%  
WC  Lleyton Hewitt          21.9%   0.4%   0.1%     0.0%  
q   Filip Krajinovic        59.1%  18.6%   1.1%     0.0%  
q   Nicolas Devilder        40.9%   9.9%   0.4%     0.0%  
q   Michael Berrer          30.1%  17.8%   1.2%     0.0%  
30  Jurgen Melzer           69.9%  53.7%   6.5%     0.0%  
                                                          
    Player                    R64    R32    R16        W  
22  Andreas Seppi           56.5%  33.5%  16.1%     0.0%  
    Nikolay Davydenko       43.5%  23.0%   9.8%     0.0%  
    Mikhail Kukushkin       49.4%  21.3%   8.4%     0.0%  
    Ernests Gulbis          50.6%  22.2%   8.9%     0.0%  
q   Igor Sijsling           51.1%  15.2%   6.2%     0.0%  
    Gilles Muller           48.9%  14.3%   5.7%     0.0%  
    Steve Darcis            24.1%  12.3%   5.1%     0.0%  
14  Fernando Verdasco       75.9%  58.2%  39.9%     0.2%  
                                                          
    Player                    R64    R32    R16        W  
11  Gilles Simon            73.8%  59.4%  32.4%     0.3%  
    Ryan Harrison           26.2%  15.7%   5.1%     0.0%  
    Xavier Malisse          70.7%  20.5%   6.0%     0.0%  
WC  Brian Baker             29.3%   4.4%   0.7%     0.0%  
    Pablo Andujar           57.2%  13.7%   4.5%     0.0%  
    Victor Hanescu          42.8%   8.3%   2.3%     0.0%  
    Flavio Cipolla          15.4%   7.4%   2.1%     0.0%  
18  Stanislas Wawrinka      84.6%  70.6%  46.8%     0.8%  
                                                          
    Player                    R64    R32    R16        W  
28  Viktor Troicki          41.9%  25.6%   6.2%     0.0%  
    Thomaz Bellucci         58.1%  39.7%  11.8%     0.0%  
    Fabio Fognini           54.9%  20.2%   3.9%     0.0%  
WC  Adrian Mannarino        45.1%  14.5%   2.4%     0.0%  
    Cedrik-Marcel Stebe     58.0%   9.3%   3.9%     0.0%  
    Joao Souza              42.0%   5.2%   1.8%     0.0%  
q   Andrey Kuznetsov        10.2%   5.1%   2.0%     0.0%  
5   Jo-Wilfried Tsonga      89.8%  80.5%  68.0%     4.5%  
                                                          
    Player                    R64    R32    R16        W  
3   Roger Federer           93.5%  81.7%  73.8%    12.0%  
    Tobias Kamke             6.5%   2.2%   0.8%     0.0%  
    Adrian Ungur            24.7%   2.0%   0.8%     0.0%  
    David Nalbandian        75.3%  14.1%   9.0%     0.1%  
    Frank Dancevic          43.6%  17.7%   2.1%     0.0%  
    Martin Klizan           56.4%  25.9%   3.8%     0.0%  
    Nicolas Mahut           27.8%  11.0%   1.0%     0.0%  
26  Andy Roddick            72.2%  45.4%   8.7%     0.0%  
                                                          
    Player                    R64    R32    R16        W  
23  Radek Stepanek          46.6%  27.7%  13.2%     0.0%  
LL  David Goffin            53.4%  33.8%  17.3%     0.0%  
WC  Arnaud Clement          36.0%  10.9%   3.3%     0.0%  
    Alex Bogomolov Jr.      64.0%  27.5%  11.8%     0.0%  
    Karol Beck              33.9%   9.3%   3.1%     0.0%  
    Lukasz Kubot            66.1%  27.4%  13.6%     0.0%  
q   Florent Serra           26.0%  11.9%   4.7%     0.0%  
15  Feliciano Lopez         74.0%  51.4%  32.9%     0.0%  
                                                          
    Player                    R64    R32    R16        W  
9   Juan Martin Del Potro   87.7%  78.8%  63.9%     3.2%  
    Albert Montanes         12.3%   6.7%   2.6%     0.0%  
    E. Roger-Vasselin       50.0%   7.3%   2.6%     0.0%  
    Vasek Pospisil          50.0%   7.2%   2.5%     0.0%  
    Juan Carlos Ferrero     63.6%  27.3%   6.6%     0.0%  
WC  J. Dasnieres De Veigy   36.4%  11.3%   2.0%     0.0%  
q   D. Munoz-De La Nava     21.4%   7.9%   1.2%     0.0%  
21  Marin Cilic             78.6%  53.5%  18.5%     0.1%  
                                                          
    Player                    R64    R32    R16        W  
31  Kevin Anderson          70.0%  50.1%  15.9%     0.0%  
    Rui Machado             30.0%  15.8%   2.9%     0.0%  
WC  Eric Prodon             41.2%  12.2%   1.8%     0.0%  
q   Horacio Zeballos        58.8%  21.9%   4.3%     0.0%  
    Michael Llodra          46.9%  10.0%   5.2%     0.0%  
    Guillermo Garcia-Lopez  53.1%  12.5%   6.8%     0.0%  
    Dudi Sela               12.3%   4.8%   2.1%     0.0%  
7   Tomas Berdych           87.7%  72.6%  61.0%     2.3%  
                                                          
    Player                    R64    R32    R16        W  
6   David Ferrer            84.0%  70.2%  55.9%     2.4%  
    Lukas Lacko             16.0%   7.8%   3.4%     0.0%  
    Benoit Paire            49.1%  10.7%   4.7%     0.0%  
    Albert Ramos            50.9%  11.2%   5.0%     0.0%  
    Ivan Dodig              56.2%  31.3%  10.5%     0.0%  
    Robin Haase             43.8%  21.6%   6.2%     0.0%  
    James Blake             31.1%  10.6%   2.0%     0.0%  
27  Mikhail Youzhny         68.9%  36.5%  12.2%     0.0%  
                                                          
    Player                    R64    R32    R16        W  
20  Marcel Granollers       66.9%  44.7%  22.7%     0.1%  
q   Joao Sousa              33.1%  16.4%   5.9%     0.0%  
    Malek Jaziri            45.3%  16.6%   5.6%     0.0%  
    Philipp Petzschner      54.7%  22.3%   8.6%     0.0%  
WC  Paul-Henri Mathieu      50.0%  11.4%   3.7%     0.0%  
    Bjorn Phau              50.0%  11.4%   3.6%     0.0%  
q   Rogerio Dutra Silva     18.5%   9.7%   3.4%     0.0%  
10  John Isner              81.5%  67.5%  46.5%     0.5%  
                                                          
    Player                    R64    R32    R16        W  
16  Alexandr Dolgopolov     70.8%  58.8%  27.9%     0.2%  
    Sergiy Stakhovsky       29.3%  19.4%   5.7%     0.0%  
    Filippo Volandri        62.9%  15.5%   3.2%     0.0%  
q   Tommy Haas              37.1%   6.2%   0.9%     0.0%  
    Donald Young            43.2%   9.8%   3.8%     0.0%  
    Grigor DiMitrov         56.8%  15.5%   6.8%     0.0%  
q   Jurgen Zopp             17.8%   8.5%   3.3%     0.0%  
17  Richard Gasquet         82.2%  66.2%  48.5%     1.4%  
                                                          
    Player                    R64    R32    R16        W  
25  Bernard Tomic           73.8%  47.6%  17.1%     0.1%  
q   Andreas Haider-Maurer   26.2%  10.4%   1.9%     0.0%  
    Santiago Giraldo        63.3%  29.5%   8.5%     0.0%  
    Alejandro Falla         36.7%  12.5%   2.4%     0.0%  
    Jarkko Nieminen         48.3%   8.4%   3.2%     0.0%  
    Igor Andreev            51.7%   9.8%   3.8%     0.0%  
    Tatsuma Ito              8.9%   3.4%   0.9%     0.0%  
4   Andy Murray             91.1%  78.4%  62.1%     3.8%  
                                                          
    Player                    R64    R32    R16        W  
8   Janko Tipsarevic        82.5%  72.8%  64.6%     5.7%  
    Sam Querrey             17.5%  10.7%   6.7%     0.0%  
    Jeremy Chardy           64.0%  12.2%   6.9%     0.0%  
    Yen-Hsun Lu             36.0%   4.3%   1.8%     0.0%  
    Dmitry Tursunov         49.3%  17.2%   2.5%     0.0%  
    Go Soeda                50.7%  17.9%   2.5%     0.0%  
q   Mischa Zverev           34.1%  18.6%   3.1%     0.0%  
29  Julien Benneteau        65.9%  46.3%  11.8%     0.0%  
                                                          
    Player                    R64    R32    R16        W  
24  Philipp Kohlschreiber   70.5%  47.1%  23.0%     0.1%  
    Matthew Ebden           29.5%  13.5%   3.9%     0.0%  
    Olivier Rochus          41.2%  14.2%   4.1%     0.0%  
    Leonardo Mayer          58.8%  25.1%   9.2%     0.0%  
    Juan Ignacio Chela      29.5%   9.0%   3.6%     0.0%  
    Marcos Baghdatis        70.5%  34.8%  21.1%     0.1%  
    Paolo Lorenzi           21.6%   7.0%   2.5%     0.0%  
12  Nicolas Almagro         78.4%  49.2%  32.5%     0.2%  
                                                          
    Player                    R64    R32    R16        W  
13  Juan Monaco             69.3%  48.7%  23.3%     0.1%  
WC  Guillaume Rufin         30.7%  15.7%   4.9%     0.0%  
    Lukas Rosol             52.5%  19.2%   6.0%     0.0%  
    Carlos Berlocq          47.5%  16.4%   4.9%     0.0%  
q   Jesse Levine            50.8%  10.1%   3.2%     0.0%  
    Benjamin Becker         49.2%   9.6%   2.9%     0.0%  
    Ruben Ramirez Hidalgo   13.4%   6.3%   1.8%     0.0%  
19  Milos Raonic            86.6%  74.0%  52.9%     0.6%  
                                                          
    Player                    R64    R32    R16        W  
32  Florian Mayer           71.3%  50.2%   7.5%     0.1%  
    Daniel Gimeno-Traver    28.7%  13.8%   1.0%     0.0%  
q   Eduardo Schwank         43.4%  14.2%   1.0%     0.0%  
    Ivo Karlovic            56.6%  21.8%   1.9%     0.0%  
    Igor Kunitsyn           31.7%   1.3%   0.4%     0.0%  
    Denis Istomin           68.3%   4.9%   2.2%     0.0%  
    Simone Bolelli           3.8%   1.8%   0.7%     0.0%  
2   Rafael Nadal            96.2%  92.1%  85.3%    30.4%

 

The Luck of the (2012 French Open) Draw

Without a single player setting foot on a match court, many players have already seen their chances of winning the French Open change quite a bit.

A Grand Slam draw can give, and it can take away.  Novak Djokovic is set to player Roger Federer in the semifinals (again), while Rafael Nadal won’t have to play either until the final.  Potito Starace will have to beat Novak Djokovic in order to reach the second round, while many of his unseeded fellow players have only to defeat a qualifier.  Life isn’t fair.

At every stage of the draw, there are winners and losers.  As I did last year, we can quantify the impact of the draw by comparing each player’s probability of reaching each round before and after the draw was set.  For instance, before the draw was set, Starace had a 66% chance of facing another unseeded player and a decent chance of reaching the second or third round.  Now that the draw was set, he might as well book his flight home.

To measure the impact, I used expected prize money, which wraps up in one number the probability that a player reaches each round.  For instance, Roger Federer was expected to win 329,000 euros before the draw was set; even with the unfortunate semifinal pairing, he’s still on track for roughly 329,000 euros.  Nadal saw a 3% improvement in expected prize money, largely because Fed and Djok are elsewhere, while Djokovic’s number stayed the same.  Yes, Fed in the semis is a rough draw, but Novak gets the benefit of a relatively easy path to the semis, with men like Jurgen Melzer and Fernando Verdasco standing in his way.

The Winners

Of the seeded players, the biggest winner of the draw was John Isner.  (This is a case where life might be fair–this is the guy who drew Nadal in last year’s first round.)  Isner’s expected prize money increased from 71,400 to 92,200, nearly a 30% jump.  Until he faces David Ferrer in the round of 16, there’s little standing in his way–and even Ferrer pales in comparison to some of the other top eight players who Isner could have drawn.

The other big winner is Richard Gasquet, whose expected prize money increased from 102,600 to 125,700.  While he is seeded outside of the top 16, his probable third-round opponent is the #16 seed Alexander Dolgopolov.  Numerically, anyway, you can’t get any luckier than that.

Taking into account the entire draw, no one got luckier than Alex Bogomolov Jr, whose expected takings rose from 26,600 to 36,000.  Bogie isn’t expected to get far, but he’ll face Arnaud Clement, then probably Radek Stepanek and Feliciano Lopez.  As Starace can tell you, it could be much worse.

The Losers

It’s a bad year for Italians at the French.  Among the top four worst draws–all players who lost about one-quarter of their expected prize money this morning–not only Starace but also Simone Bolelli are included.  After all, Bolelli drew Nadal!

The toughest luck among seeds fell to Viktor Troicki (loser of 26% of his expected prize money) and Gilles Simon (loser of 18%).  Both players are in Djokovic’s quarter, putting an effective end to any title hopes they may have … if they even make it that far.  Troicki drew one of the toughest clay-courters from the unseeded pool, Thomaz Bellucci, and if he gets to the second round, would play Adrian Mannarino or Fabio Fognini.  After that? Jo-Wilfried Tsonga.

In actuality, Simon might have the toughest road.  His possible second-rounder is Brian Baker, the man who has taken Nice by storm.  My rankings don’t give Baker much credit yet–after all, he only has a recent few pro matches under his belt under Nice goes on the books–so it’s likely that he is more dangerous than my numbers give him credit for.  Simon’s already unfortunate French Open draw is worse than it looks.

How Does the Blue Clay Play?

If someone told you about an event where Rafael Nadal crashed out to a non-contender, Milos Raonic made a statement, and the final pitted Tomas Berdych against Roger Federer, you’d be forgiven for assuming the event was played on a very fast court. All of those things happened last week in Madrid on a surface that has at least some things in common with clay.

Given the tournament results, it’s no surprise to discover that statistically, the Madrid courts didn’t play like the old-fashioned red stuff. The stats from this year’s event at Caja Majica are a significant departure from those in past years, and suggest that the blue clay resembles a hard court more than it does European dirt.

Let’s start with aces. Aces are the stat most affected by surface, given the small difference in serve speed and bounce trajectory that can turn a returnable offering into an unreachable one. Of the 29 ATP tournaments played so far this year, Madrid ranks 10th in ace percentage after making adjustments for the players in the field and how many matches each one played. In fact, taking these adjustments into account, the ace rate in Madrid was almost indistinguishable from that of the indoor San Jose tourney!

(For a bit more background on methodology and more tourney-by-tourney comparison, see this article from last September.)

This is a huge departure for Madrid. The tournament has always had a reputation for playing a bit fast, given the altitude compared to Monte Carlo, Barcelona, Rome, and Paris, but that has long been a minor difference, at least when it comes to ace counts. In 2011, Madrid’s ace rate ranked 22nd of the season’s first 29 events, just ahead of Acupulco and behind Munich, Casablanca, and Santiago. 2010 was almost exactly the same, with Madrid coming in 23rd of these 29 events.

Another way of estimating court speed is by looking at the percentage of points won by the server. Even on points where the returner gets the ball back in play, a fast court should generate weaker returns and more third-shot winners. In this department, Madrid once again ranks among this year’s faster events. As in ace rate, it is #10 of 29 on the list, just behind San Jose and ahead of the hard court events in Chennai, Auckland, and Brisbane.

I can’t say whether it’s right or wrong to have a Masters-level event on an unusual surface, but I can say, based on these numbers, that the blue clay hardly plays like clay at all.