Category: WTA

Halep’s Draw, Serena’s H2Hs, American Advancement

When the US Open Women’s draw was released on Friday, things looked awfully bright for Caroline Wozniacki.  With Maria Sharapova‘s withdrawal, Sara Errani became the #4 seed, meaning that one spot in the semis belonged to Errani–or, more likely, someone who knocked her off along the way.

But Wozniacki is no lock herself.  11 of her last 12 losses have come to players outside the top 20.  She’ll have to do much better than that to take advantage of her position in the Errani quarter.

To find a dark horse for that semifinal spot, look no further than Wozniacki’s latest conqueror, Simona Halep.  Halep crushed Petra Kvitova yesterday in New Haven, marking her fourth title of the year on three (!) different surfaces.  In her last 38 matches, the only player to beat her in straight sets has been Serena Williams.

Halep’s path to the semifinal goes starts with Heather Watson and either Donna Vekic or Mariana Duque Marino, then a possible third-rounder with Maria Kirilenko, whom she has never played.  Errani would be her fourth-round opponent if she lives up to her seeding, though that section is completely up for grabs. Wozniacki–who Halep beat on Friday in straight sets–is the presumptive quarterfinalist.

Strangely enough, Halep is one of the few players in the draw with a reason to fear Errani on hard courts.  In Miami this year, the Italian routed her 6-1 6-0.

—

Yesterday, when Serena Williams was asked about her rivalry with Victoria Azarenka, she said, “I think the head-to-head is close.”  It’s not: Serena has won 12 of their 15 meetings.  While Vika has won two of the last three–including each of the last two on hard courts–the American won the ten before that.

Given Serena’s dominance over the rest of the WTA, one might reasonably ask whether an 80% winning percentage actually does constitute “close” for the world #1.  Sure enough, there are few players who have topped that.

In her career, Serena has faced 42 different opponents at least five times.  Only 13 of those have won one-quarter or more of their meetings, and only five of those remain active.  To go even further, three of those five–Venus Williams, Nadia Petrova, and Francesca Schiavone–no longer figure to threaten Serena at all.

The remaining two players are Jelena Jankovic (4 wins in 10 meetings) and Samantha Stosur (3 wins in 9 meetings).  Jankovic wouldn’t face Serena until the semifinals, and Stosur until the finals, even in the unlikely event either player made it that far.

Of course, there are good players who have met Serena fewer than five times, including her possible fourth-round opponent, Sloane Stephens.  Of the 108 active players who have ever faced Williams, Sloane is one of only five who have won at least half of their meetings with her.

—

The three US women who qualified for the main draw pushed the total number of Americans on the women’s side to 19, the highest number since 2006.  Between those qualifiers and a few long-shot wild cards, most of the 19 will be gone a week from now.  But even accounting for plenty of attrition, the American force could continue to shine brighter than they have for nearly a decade.

Based on my draw forecast (which is in turn based on WTA rankings), we should expect to see between eight and nine US women in the second round.  Eight wouldn’t be terribly impressive–that mark was reached in both 2009 and 2011, but nine would represent a step forward, however incremental.  The last time nine or more American women reached the second round was when ten did so in 2005–and that accomplishment required 23 US players in the main draw.

My forecasts predict about four American women in the third round–equal to last year’s mark, and one short of 2011’s.  But if the home favorites can score a couple of upsets and get six women into the round of 32, it would be the first time since 2004, when eight US women made it that far.

If the American women do make a strong showing, there’s an added bonus: It might help us ignore the plight of the American men.

Trends and Perspective on WTA Retirements and Withdrawals

Yesterday, there was no women’s singles at Indian Wells. Both Victoria Azarenka and Sam Stosur pulled out of their quarterfinal matches, presenting a very obvious target for anyone concerned about an injury bug in women’s tennis.

Last year, WTA retirements hit an all-time high of 4.8% of tour-level matches, almost a full percentage point above the 3.9% of matches that were not completed in 2006.  While part of the injury total was due to stomach bugs in China and food poisoning at Indian Wells, the overall trend has been upward for about 30 years:

WTArets

While it’s less clear that players are any more likely to pull out of Grand Slam matches (the dark red line in the graph above), there’s no doubt that more WTA matches are ending due to injury than they did 10, 20, or 30 years ago.

In a moment, I’ll explain why this is happening, and why the trend is unlikely to reverse itself anytime soon.  But first, some perspective on yesterday’s programming disaster.

Since there was nothing else to talk about yesterday in the world of women’s tennis, it was inevitable that the subject of injuries dominated. (Thanks to Federer vs. Nadal on the card, it wasn’t nearly as bad as it could have been.) Taking a tournament-wide view, though, this year’s Indian Wells WTA event has been a positive on the health front.

Women’s tennis has seen more than 1 in 50 tour-level matches end with W/O or RET in the score for more than 15 years.  Yesterday’s two withdrawals were the first two incomplete matches of the entire event–including qualifying!  Assuming we get through the semifinals and final without any further problems, that’s 93 of 95 (97.9%) of main draw matches complete, and 129 of 131 (98.5%) of main draw and qualifying matches complete.  Last year, while food poisoning dominated the headlines, there were at least three injury-related retirements from the singles draw, and two years ago, there were five.

These two quarterfinal withdrawals were bad news for television and fans, but they don’t represent a trend.

High stakes, high risk

While Indian Wells has been mostly injury-free, it also shouldn’t be seen as a trend in the positive direction.  WTA players (and ATPers, for the same reasons) are going to keep showing up at tournaments less than 100%, developing health problems midway through tournaments, and generally not finishing all the matches they start.

This isn’t because of too many hard courts, slower balls, mandatory events, doping, or even runaway racquet technology.  It’s because the financial stakes in tennis–and with it, severe inequality in the ranks–are climbing even faster than the injury rate.  The level of fitness required to compete at the highest level is always increasing, and players are forced to choose between trying to keep up or probably falling away.

A simpler example of this phenomenon, and one that makes it easier to illustrate the point, is in competitive distance running.  Marathoners rarely run more than two marathons per year, and there is very little room at the top.  Run a marathon in 2:04 and you’re a superstar. 2:05 or 2:06 and the sponsors will keep supporting you.  If you can’t break 2:10, you’re probably working full-time at a local shoe store.

The most straightforward way to improve your marathon time is to train harder, whether that means more mileage over a several-month training period or more aggressive workouts.  When the choice is between 2:05 and oblivion, the incentives are heavily structured toward overly aggressive training.  There’s not much difference between finishing with a 2:10 compared to overtraining, getting injured, and not finishing at all.

Tennis, of course, is a bit more forgiving.  You don’t need to be one of the top 10 in the world to make a decent living, but then again, to remain in the top 10, you must consistently beat players on the fringes of the top 100, where the incentives are not that different from those in distance running.

As the stakes increase, players are more willing to skirt the edge between hard training and over training.  And while players are getting closer to that line, they are hardly going too far–at least according to their own incentives.  Sure, we’d like to have seen Vika play yesterday, but a few retirements over the course of the year isn’t going to stop her from regaining the #1 ranking.  Two years ago, she pulled out of her quarterfinal with Caroline Wozniacki after only three games–and then started a twelve-match winning streak the following week.

If there were more matches on clay, players would simply push themselves harder on clay courts.  (Anyway, there is almost exactly the same percentage of WTA retirements on clay as there are on hard.)  Same thing if the balls played faster.  If there were fewer mandatory events, we’d see top players engaging in longer periods of hard training. Probably more exhibitions, too.

There are no incentives–nor should there be–for players to stay healthy for the duration of 100% of their matches.  If we want the best players in the world to entertain us with the best possible tennis they can play, retirements and withdrawals are something we’ll have to learn to accept.  We won’t get one without the other.

WTA and ITF Results on TennisAbstract!

I hope that by now, you’ve taken advantage of the wealth of ATP results and stats at TennisAbstract.com.  This week, I’ve expanded the site to include women’s tennis–a lot of women’s tennis.

Not only does TA now contain all the matches from the entire history of the WTA  and Fed Cup, but it is also bursting at the seams with lower-level ITFs, all the way down to 10k’s and satellites.  You can track the progress of Annika Beck, keep tabs on Melanie Oudin‘s resurgence, or simply take a look into the history of a long-running event.

(If ITFs and men’s futures are your thing, you can always get a one-page look at this week’s events–men and women–from the TA homepage.  Players in those draws are linked to their TA results pages, as well.)

All told, the site now contains 317,815 matches across 12,807 tournaments.  That’s about 13,000 players, of whom about half have WTA ranking data.

I’ve also started churning out some additional data on the ladies.  The WTA Rankings by Age report shows the highest-ranked teenagers, under-21s, under-23s, and older players, while the WTA H2H Matrix shows the head-to-head records of the WTA top 15 in one place.  And there’s more to come.

To get started, just click some clinks in those reports, or use the search box on the front page (or almost any other page) to look up the WTA player of your choice.  Enjoy!

2012 US Open Women’s Projections

Here are my pre-tournament odds for the 2012 US Open.  For some background reading, follow the links for more on my player rating system, current rankings, and more on how I simulate tournaments.

    Player                         R64    R32    R16        W  
1   Victoria Azarenka            92.6%  83.5%  70.0%    12.5%  
    Alexandra Panova              7.4%   3.2%   1.0%     0.0%  
    Barbora Zahlavova Strycova   46.8%   6.0%   2.1%     0.0%  
    Kirsten Flipkens             53.2%   7.3%   2.7%     0.0%  
    Su-Wei Hsieh                 56.4%  24.1%   5.4%     0.0%  
    Magdalena Rybarikova         43.6%  16.0%   2.9%     0.0%  
    Virginie Razzano             41.4%  22.8%   5.2%     0.0%  
28  Jie Zheng                    58.6%  37.1%  10.6%     0.2%  

    Player                         R64    R32    R16        W  
18  Julia Goerges                80.7%  66.0%  37.5%     0.8%  
    Kristyna Pliskova            19.3%  10.1%   2.6%     0.0%  
    Mandy Minella                50.2%  12.0%   3.0%     0.0%  
    Olivia Rogowska              49.8%  11.9%   2.9%     0.0%  
    Stephanie Foretz Gacon       43.0%   7.4%   2.0%     0.0%  
    Anna Tatishvili              57.0%  12.4%   4.0%     0.0%  
    Sorana Cirstea               40.3%  30.8%  16.9%     0.2%  
16  Sabine Lisicki               59.7%  49.4%  31.2%     0.8%  

    Player                         R64    R32    R16        W  
9   Na Li                        85.7%  75.7%  41.9%     4.6%  
    Heather Watson               14.3%   8.0%   1.6%     0.0%  
    Lesia Tsurenko               45.0%   6.6%   1.0%     0.0%  
    Casey Dellacqua              55.0%   9.7%   1.8%     0.0%  
    Samantha Crawford            14.0%   0.5%   0.0%     0.0%  
    Laura Robson                 86.0%  14.2%   3.6%     0.0%  
    Victoria Duval                0.9%   0.1%   0.0%     0.0%  
23  Kim Clijsters                99.1%  85.3%  50.1%     5.5%  

    Player                         R64    R32    R16        W  
31  Varvara Lepchenko            66.9%  44.1%  15.7%     0.0%  
    Mathilde Johansson           33.1%  16.1%   3.7%     0.0%  
    Anastasia Rodionova          55.9%  23.4%   5.9%     0.0%  
    Julia Cohen                  44.1%  16.4%   3.5%     0.0%  
    Edina Gallovits-Hall         44.2%   7.1%   2.7%     0.0%  
    Stefanie Voegele             55.8%  10.8%   4.7%     0.0%  
    Petra Martic                 25.5%  17.6%  10.7%     0.0%  
7   Samantha Stosur              74.5%  64.5%  53.0%     2.1%  

    Player                         R64    R32    R16        W  
3   Maria Sharapova              86.5%  77.7%  67.0%     9.3%  
    Melinda Czink                13.5%   7.9%   4.1%     0.0%  
    Lourdes Dominguez Lino       48.9%   6.9%   3.0%     0.0%  
    Sesil Karatantcheva          51.1%   7.4%   3.3%     0.0%  
    Timea Bacsinszky             70.8%  19.4%   2.5%     0.0%  
    Mallory Burdette             29.2%   3.9%   0.3%     0.0%  
    Lucie Hradecka               38.0%  27.1%   5.7%     0.0%  
27  Anabel Medina Garrigues      62.0%  49.6%  14.1%     0.1%  

    Player                         R64    R32    R16        W  
19  Nadia Petrova                67.0%  36.1%  19.5%     0.2%  
    Jarmila Gajdosova            33.0%  12.0%   4.4%     0.0%  
    Simona Halep                 49.9%  25.8%  12.8%     0.1%  
    Iveta Benesova               50.1%  26.1%  13.0%     0.1%  
    Alexandra Cadantu            21.3%   4.5%   1.0%     0.0%  
    Aleksandra Wozniak           78.7%  37.7%  18.7%     0.2%  
    Melanie Oudin                30.9%  13.9%   5.3%     0.0%  
15  Lucie Safarova               69.1%  43.9%  25.4%     0.4%  

    Player                         R64    R32    R16        W  
11  Marion Bartoli               78.4%  46.4%  28.9%     1.2%  
    Jamie Hampton                21.6%   6.4%   2.1%     0.0%  
    Romina Oprandi               24.5%   7.1%   2.3%     0.0%  
    Andrea Petkovic              75.5%  40.2%  23.9%     0.7%  
    Kristina Mladenovic          37.5%   7.2%   1.4%     0.0%  
    Marina Erakovic              62.5%  17.6%   4.9%     0.0%  
    Daniela Hantuchova           48.8%  36.5%  17.6%     0.4%  
17  Anastasia Pavlyuchenkova     51.2%  38.7%  18.9%     0.5%  

    Player                         R64    R32    R16        W  
25  Yanina Wickmayer             82.8%  64.6%  26.3%     0.6%  
    Julia Glushko                17.2%   7.3%   1.2%     0.0%  
    Pauline Parmentier           45.4%  11.9%   2.1%     0.0%  
    Michaella Krajicek           54.6%  16.2%   3.3%     0.0%  
    Nicole Gibbs                 23.5%   1.7%   0.3%     0.0%  
    Alize Cornet                 76.5%  15.0%   5.8%     0.0%  
    Polona Hercog                15.7%   9.2%   3.9%     0.0%  
5   Petra Kvitova                84.3%  74.0%  57.1%     6.9%  

    Player                         R64    R32    R16        W  
8   Caroline Wozniacki           85.1%  72.5%  52.5%     4.1%  
    Irina-Camelia Begu           14.9%   7.6%   2.4%     0.0%  
    Silvia Soler-Espinosa        57.0%  12.3%   4.3%     0.0%  
    Alla Kudryavtseva            43.0%   7.6%   2.2%     0.0%  
    Tsvetana Pironkova           68.7%  48.3%  22.8%     0.5%  
    Camila Giorgi                31.3%  16.4%   5.2%     0.0%  
    Ayumi Morita                 37.6%  10.7%   2.6%     0.0%  
26  Monica Niculescu             62.4%  24.5%   8.0%     0.0%  

    Player                         R64    R32    R16        W  
22  Francesca Schiavone          55.4%  41.9%  18.9%     0.2%  
    Sloane Stephens              44.6%  31.9%  12.9%     0.1%  
    Akgul Amanmuradova           52.9%  14.2%   3.3%     0.0%  
    Tatjana Malek                47.1%  12.0%   2.5%     0.0%  
    Kimiko Date-Krumm            29.2%   5.8%   1.8%     0.0%  
    Sofia Arvidsson              70.8%  25.3%  13.4%     0.1%  
    Elina Svitolina              13.8%   4.5%   1.4%     0.0%  
12  Ana Ivanovic                 86.2%  64.4%  45.8%     1.6%  

    Player                         R64    R32    R16        W  
14  Maria Kirilenko              67.6%  50.9%  31.6%     0.6%  
    Chanelle Scheepers           32.4%  19.5%   8.6%     0.0%  
    Agnes Szavay                 16.2%   1.4%   0.2%     0.0%  
    Greta Arn                    83.8%  28.2%  11.2%     0.0%  
    Galina Voskoboeva            59.1%  30.2%  15.0%     0.1%  
    Arantxa Rus                  40.9%  17.3%   7.1%     0.0%  
    Andrea Hlavackova            30.0%  11.4%   4.0%     0.0%  
24  Klara Zakopalova             70.0%  41.1%  22.3%     0.2%  

    Player                         R64    R32    R16        W  
32  Shuai Peng                   57.6%  25.3%   5.2%     0.1%  
    Elena Vesnina                42.4%  15.8%   2.6%     0.0%  
    Ekaterina Makarova           80.0%  52.4%  14.9%     0.8%  
    Eleni Daniilidou             20.0%   6.5%   0.8%     0.0%  
    Mirjana Lucic                35.6%   3.0%   0.8%     0.0%  
    Maria Jose Martinez Sanchez  64.4%   8.7%   3.4%     0.0%  
    Coco Vandeweghe               8.2%   4.1%   1.3%     0.0%  
4   Serena Williams              91.8%  84.2%  70.9%    26.1%  

    Player                         R64    R32    R16        W  
6   Angelique Kerber             88.6%  65.7%  48.5%     6.0%  
    Anne Keothavong              11.4%   3.3%   1.0%     0.0%  
    Bethanie Mattek-Sands        30.1%   6.3%   2.4%     0.0%  
    Venus Williams               69.9%  24.7%  13.9%     0.4%  
    Johanna Konta                42.7%  10.2%   1.6%     0.0%  
    Timea Babos                  57.3%  16.6%   3.3%     0.0%  
    Olga Govortsova              18.2%   8.4%   1.4%     0.0%  
29  Tamira Paszek                81.8%  64.8%  27.9%     1.1%  

    Player                         R64    R32    R16        W  
21  Christina McHale             75.7%  61.4%  41.8%     1.0%  
    Kiki Bertens                 24.3%  14.2%   6.0%     0.0%  
    Olga Puchkova                39.7%   7.9%   2.4%     0.0%  
    Irina Falconi                60.3%  16.5%   6.5%     0.0%  
    Vera Dushevina               68.3%  27.2%  10.4%     0.0%  
    Nastassja Burnett            31.7%   7.5%   1.7%     0.0%  
    Garbine Muguruza             36.1%  20.5%   7.9%     0.0%  
10  Sara Errani                  63.9%  44.9%  23.3%     0.2%  

    Player                         R64    R32    R16        W  
13  Dominika Cibulkova           73.9%  54.9%  35.7%     1.3%  
    Johanna Larsson              26.1%  13.2%   5.2%     0.0%  
    Bojana Jovanovski            44.2%  13.1%   4.8%     0.0%  
    Mona Barthel                 55.8%  18.8%   8.0%     0.0%  
    Vania King                   54.1%  25.1%  11.3%     0.1%  
    Yaroslava Shvedova           45.9%  19.7%   8.2%     0.0%  
    Urszula Radwanska            45.1%  23.7%  10.9%     0.1%  
20  Roberta Vinci                54.9%  31.5%  15.9%     0.2%  

    Player                         R64    R32    R16        W  
30  Jelena Jankovic              60.9%  40.0%  14.4%     0.2%  
    Kateryna Bondarenko          39.1%  21.7%   6.0%     0.0%  
    Lara Arruabarrena-Vecino     25.7%   5.6%   0.7%     0.0%  
    Shahar Peer                  74.3%  32.6%   9.1%     0.0%  
    Ksenia Pervak                47.1%  10.4%   4.7%     0.0%  
    Carla Suarez Navarro         52.9%  12.6%   6.1%     0.0%  
    Nina Bratchikova             11.3%   4.2%   1.4%     0.0%  
2   Agnieszka Radwanska          88.7%  72.7%  57.6%     6.7%

2012 Olympics Round of 16 Forecasts

Here are my forecasts for the remaining 16 players in both Olympics singles draws.  Note that Djokovic has opened up a bigger gap over Federer.  Novak is aided by Berdych’s upset, while Federer is still likely to play the top seeds in his half.

On the women’s side, the third quarter is a crowded one, with Clijsters, Sharapova, and two dangerous floaters in Ivanovic and Lisicki.

For more background, you can see my initial forecasts, (almost) current rankings, and methodology.

Men:

Player                       QF     SF      F      W  
(1)Roger Federer          85.3%  64.5%  45.1%  25.7%  
Denis Istomin             14.7%   5.0%   1.5%   0.3%  
(10)John Isner            53.5%  16.9%   7.5%   2.4%  
(7)Janko Tipsarevic       46.5%  13.5%   5.6%   1.7%  
(4)David Ferrer           63.3%  36.3%  16.2%   6.7%  
(15)Kei Nishikori         36.7%  16.0%   5.2%   1.6%  
(12)Gilles Simon          32.3%  11.7%   3.3%   0.8%  
(8)Juan Martin Del Potro  67.7%  36.0%  15.5%   6.2%  

Player                       QF     SF      F      W  
Steve Darcis              39.5%   8.9%   1.5%   0.3%  
(11)Nicolas Almagro       60.5%  18.1%   4.2%   1.3%  
Marcos Baghdatis          22.7%  11.9%   2.7%   0.7%  
(3)Andy Murray            77.3%  61.1%  29.8%  16.4%  
(5)Jo-Wilfried Tsonga     67.5%  23.3%  12.0%   5.4%  
Feliciano Lopez           32.5%   6.9%   2.4%   0.7%  
(WC)Lleyton Hewitt         4.6%   0.6%   0.1%   0.0%  
(2)Novak Djokovic         95.4%  69.3%  47.3%  29.7%

Women:

Player                 QF     SF      F      W  
Victoria Azarenka   78.9%  53.3%  28.2%  18.0%  
Nadia Petrova       21.1%   7.9%   1.9%   0.6%  
Venus Williams      16.8%   2.5%   0.3%   0.1%  
Angelique Kerber    83.2%  36.3%  14.8%   7.6%  
Serena Williams     75.9%  56.2%  36.9%  26.2%  
Vera Zvonareva      24.1%  11.5%   4.4%   1.9%  
Daniela Hantuchova  36.2%   9.1%   2.9%   1.1%  
Caroline Wozniacki  63.8%  23.2%  10.6%   5.3%  

Player                 QF     SF      F      W  
Kim Clijsters       62.5%  33.2%  20.3%   8.9%  
Ana Ivanovic        37.5%  15.4%   7.4%   2.5%  
Sabine Lisicki      36.8%  15.7%   7.7%   2.5%  
Maria Sharapova     63.2%  35.6%  22.2%  10.0%  
Petra Kvitova       65.5%  45.7%  23.9%  10.2%  
Flavia Pennetta     34.5%  18.9%   7.0%   1.9%  
Maria Kirilenko     47.5%  16.2%   5.0%   1.2%  
Julia Goerges       52.5%  19.3%   6.6%   1.8%

2012 Olympics Women’s Projections

Forecasting the women’s singles event isn’t rocket science–it’s just a matter of how much you favor Serena Williams over everyone else.

My algorithm gives Serena a 22.7% chance of taking home the gold.  While the draw did her a favor, placing Kim Clijsters in the other half, it wasn’t perfect: Jelena Jankovic is a relatively difficult first round match.  With a randomized draw, Serena’s chances are nearly 25%.

Following the American is her very likely semifinal opponent, Victoria Azarenka, who I give a 18.4% chance of winning it all.  With an easier draw in the early rounds, Azarenka has a slightly better chance of making it to the semis (49.0% to 45.6%), but is less likely to come out of that showdown triumphant.

No one else has a double-digit chance of winning the tournament.  Williams and Azarenka are followed, in order, by Maria Sharapova, Agnieszka Radwanska, Petra Kvitova, and Clijsters.

Below, find the forecast for the entire field.  To see my current hard-court rankings, click here, and for some background on the system, click here.  I’ve also posted projections for the men’s singles event.

Player                         R32    R16     QF        W  
Victoria Azarenka            92.3%  79.6%  65.3%    18.4%  
Irina-Camelia Begu            7.7%   2.6%   0.8%     0.0%  
Maria Jose Martinez Sanchez  55.6%  10.6%   4.9%     0.1%  
Polona Hercog                44.4%   7.2%   3.0%     0.0%  
Anna Tatishvili              55.5%  12.3%   1.6%     0.0%  
Stephanie Vogt               44.5%   8.4%   0.9%     0.0%  
Jie Zheng                    49.9%  39.5%  11.6%     0.5%  
Nadia Petrova                50.1%  39.8%  11.8%     0.5%  
                                                           
Player                         R32    R16     QF        W  
Sara Errani                  62.9%  35.8%  13.2%     0.2%  
Venus Williams               37.1%  16.3%   4.4%     0.0%  
Marina Erakovic              45.6%  20.8%   6.1%     0.1%  
Aleksandra Wozniak           54.4%  27.1%   8.6%     0.1%  
Galina Voskoboeva            69.5%  23.6%  13.7%     0.4%  
Timea Babos                  30.5%   5.7%   2.2%     0.0%  
Petra Cetkovska              29.5%  17.0%  10.2%     0.3%  
Angelique Kerber             70.5%  53.6%  41.8%     4.9%  
                                                           
Player                         R32    R16     QF        W  
Serena Williams              83.9%  73.9%  60.2%    22.7%  
Jelena Jankovic              16.1%   9.6%   4.4%     0.2%  
Mona Barthel                 43.0%   6.3%   2.2%     0.0%  
Urszula Radwanska            57.0%  10.2%   4.2%     0.1%  
Francesca Schiavone          46.9%  18.8%   4.4%     0.2%  
Klara Zakopalova             53.1%  23.1%   6.0%     0.2%  
Sofia Arvidsson              28.1%  11.8%   2.3%     0.0%  
Vera Zvonareva               71.9%  46.3%  16.3%     1.6%  
                                                           
Player                         R32    R16     QF        W  
Na Li                        61.5%  41.9%  23.1%     2.3%  
Daniela Hantuchova           38.5%  22.0%   9.6%     0.5%  
Alize Cornet                 26.5%   5.5%   1.2%     0.0%  
Tamira Paszek                73.5%  30.6%  13.2%     0.6%  
Anabel Medina Garrigues      34.7%  10.3%   3.4%     0.0%  
Yanina Wickmayer             65.3%  27.8%  13.3%     0.7%  
Anne Keothavong              14.5%   3.9%   0.8%     0.0%  
Caroline Wozniacki           85.5%  58.0%  35.3%     4.1%  
                                                           
Player                         R32    R16     QF        W  
Samantha Stosur              81.9%  39.2%  23.9%     2.4%  
Carla Suarez Navarro         18.1%   3.3%   0.9%     0.0%  
Kim Clijsters                76.2%  48.7%  33.5%     5.6%  
Roberta Vinci                23.8%   8.8%   3.7%     0.1%  
Agnes Szavay                 21.8%   1.5%   0.1%     0.0%  
Elena Baltacha               78.2%  16.0%   2.7%     0.0%  
Christina McHale             43.9%  35.4%  13.9%     0.7%  
Ana Ivanovic                 56.1%  47.2%  21.3%     1.7%  
                                                           
Player                         R32    R16     QF        W  
Sabine Lisicki               95.1%  66.0%  31.5%     2.7%  
Ons Jabeur                    4.9%   0.5%   0.0%     0.0%  
Simona Halep                 61.6%  22.8%   7.6%     0.2%  
Yaroslava Shvedova           38.4%  10.7%   2.6%     0.0%  
Petra Martic                 37.2%   9.2%   3.2%     0.1%  
Lucie Safarova               62.8%  21.5%  10.1%     0.5%  
Shahar Peer                  18.4%   7.7%   2.6%     0.0%  
Maria Sharapova              81.6%  61.6%  42.3%     7.6%  
                                                           
Player                         R32    R16     QF        W  
Petra Kvitova                80.7%  61.8%  41.0%     6.5%  
Kateryna Bondarenko          19.3%   8.5%   2.8%     0.0%  
Su-Wei Hsieh                 42.9%  11.3%   3.8%     0.1%  
Shuai Peng                   57.1%  18.4%   7.5%     0.2%  
Sorana Cirstea               41.5%  16.9%   6.4%     0.2%  
Flavia Pennetta              58.5%  28.8%  13.2%     0.9%  
Tsvetana Pironkova           42.8%  21.5%   9.2%     0.5%  
Dominika Cibulkova           57.2%  32.8%  16.1%     1.3%  
                                                           
Player                         R32    R16     QF        W  
Maria Kirilenko              85.9%  60.6%  23.8%     1.0%  
Mariana Duque-Marino         14.1%   3.8%   0.4%     0.0%  
Silvia Soler-Espinosa        45.6%  15.3%   3.2%     0.0%  
Heather Watson               54.4%  20.3%   4.8%     0.0%  
Varvara Lepchenko            73.2%  13.1%   5.0%     0.0%  
Veronica Cepede Royg         26.8%   2.0%   0.4%     0.0%  
Julia Goerges                30.0%  23.0%  14.1%     0.7%  
Agnieszka Radwanska          70.0%  61.9%  48.2%     8.4%

The Misleading Stat Sheet

A glance at the stat sheet from Serena Williams’s third-round match against Jie Zheng suggests that Serena dominated.  23 aces to 1, 3 break point conversions to none, 54 winners to 21, 84% 2nd-serve points won to 50%, and 55% of the total points played.

Of course, according to the more important stats–games and sets–Serena didn’t dominate.  She barely snuck through, losing a first-set tiebreak and going to 9-7 in the third.

Rick Devereaux, who brought this contrast to my attention, suggests that grass-court tennis–with more clean winners and fewer unforced errors than slower-paced styles–may be responsible.  That’s certainly part of the equation.

In fact, the Serena/Zheng match highlights the limits of the traditional stat sheet, especially on a surface that particularly favors the server.  Except for winners and unforced errors, nearly every stat directly captures some aspect of serving prowess–either yours or your opponent’s.  And in an era where nearly everyone is an excellent server, it doesn’t matter much whether you’ve set down a great serving performance or merely a good one.

To get to tiebreaks (or 9-7, or 70-68), you don’t have to be as good as your opponent, you just need to be good enough to hold.  Even the “winners” stat has to do with serving dominance, since so many are third shots behind a serve.  The vast majority of the stats from Serena’s match tell us that the American was more dominant on her serve than Zheng was.  And, of course, while Zheng was good enough to hold to 6-6 and 7-7, she lost the second set fairly badly, so the stats are a weighted average of two almost-even sets and one lopsided one.

When we find a mismatch between stat sheet and scoreline, we’re usually seeing one of two things:

  1. One player was much more dominant on serve (think 4 or 5-point games instead of 6+)
  2. One player won a lot of clutch points (like deuce, on serve) — losing unimportant ones (like 40-0 on serve), thus padding her opponent’s stat sheet.

Oddly, in the men’s game, the players who we think of as most dominant on serve rarely give us mismatched score sheets like this–quite the opposite.  Note the wording: “one player was much more dominant.”  There’s no doubt John Isner can dominate on serve, but since almost all his opponents are also good servers, Isner’s weak return game means that he is often the less dominant server, winning service games at 40-30 and losing return games at 0-40 or 15-40.  In fact, Isner has won more than 20 career matches despite losing more than half of the points played!

The same reasoning doesn’t apply to Serena.  She may be as big a server (relative to her opponents) as Isner, but her return game is also world-class.  And in the WTA, there are far more weak-to-middling servers.  On grass, as Rick points out, those weak-to-middling servers are (usually) still able to hold, making it more likely that a dominant performance on paper ends at 9-7 in a deciding set.

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%

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%

Why the ATP is More Popular Than the WTA

Last night, Fernando Gonzalez played the last match of his career.  Gonzo is a fan favorite, with a historically great forehand that propelled him to finals at the 2007 Australian Open and the 2008 Olympics.  He won tour-level titles over a ten-year span.

Next month, the man in the limelight will be Ivan Ljubicic.  He doesn’t exactly qualify as a “fan favorite,” but tennis aficionados have grown to appreciate his deadly service accuracy, beautiful one-handed backhand, and intelligence on and off the court.

Men’s tennis is in the age of the veteran.  Even though we’re talking about 20-somethings and a few 30-year-olds, virtually every player at the top of the game five years ago is still in the mix today.  With the exception of Andre Agassi, every top-ranked player from the ten years is still active.

And fans love veterans.  The current state of the ATP is tailor-made for fan interest.

There are two things going on here.  One is simply a matter of familiarity.  If you lost interest in tennis for the last five years, you might be surprised to find Mario Ancic out of the game, Arnaud Clement still in it, and Andy Roddick well out of the top ten, but the cast of characters would be immediately recognizable.  It’s like a television soap opera–you only have to watch an episode or two before you’re back in the swing of things.

The other factor is what we might call the “Agassi effect.”  In the late 80’s and early 90’s, Agassi was the stereotypical brash youngster, offending the effete and challenging Wimbledon’s all-white rule.  A decade and a half later, he was perhaps the most popular player in the game, the very picture of sportsmanship and class.  Few players undergo such a radical transformation in the eyes of the public, but the general direction is very common.

Only a few years ago, Rafael Nadal was a divisive figure, mocked by many for his sleeveless tops and bulging biceps.  More recently, Novak Djokovic was widely disliked.  I’m sure detractors are still out there, but they are much quieter.  Think back to the early days of just about any veteran’s career–Andy Roddick was exciting to American fans, objectionable to most everybody else.  Lleyton Hewitt was another Agassi, and he didn’t grow out of it as quickly.

Yet for all that, can you think of a player who has gotten less popular as he ages?  Perhaps this phenomenon is unique to individual sports.  In team sports, some figures seem to attract fans, but others lose them, as they sign mega-contracts with new teams, becoming viewed as sellouts.  (Or worse, if they take the mega-contract, then never perform as well again.)

The phenomenon of gaining fans with age isn’t limited to men–veteran WTA players experience it, as well.  It seems like Kim Clijsters was better loved upon her return to the game than she was the day she retired.  Even the Williams sisters seem to have fewer detractors these days than they did several years ago.  But while the WTA has its share of vets, it has far fewer players who have persisted at the top of the game.

Only two players from the 2007 year-end top ten (Maria Sharapova and Marion Bartoli) are in the top ten of today’s WTA rankings.  Most of the WTA’s vets have hung around on the fringes of the game’s best for years.  Li Na, Sam Stosur, and Vera Zvonareva have all given us their share of highlights, but to extend my soap opera analogy, they are peripheral characters who star in a few episodes, only to disappear into the background again.  Someone who hasn’t watched women’s tennis for a few years would have a hard time catching up.

Of course, none of this is to say that men’s tennis is inherently better.  At various times in the past, the WTA has had a stronger stable of perennial stars, and when that is the case, it rakes in the ratings.  Victoria Azarenka may not be as obviously bankable as a charmer like Caroline Wozniacki or a cover girl like Maria Sharapova, but by winning consistently, she gives the women’s game a head start toward developing what the ATP possesses right now.  If a few other players rise to the challenge for more than a couple months at a time, we might do more than just talk about Djokovic, Federer, and Nadal all the time.