As with the earlier women’s semifinal, just the stats on this one between Serena Williams and Li Na.
Here are the numbers, on each player’s serve, return, and shot selection. Enjoy!
Category: WTA
Azarenka d. Pennetta: Point-by-point stats
I won’t be able to do a full recap on the women’s semifinal between Victoria Azarenka and Flavia Pennetta, but I did chart the match.
Here are point-by-point-based serve, return, and rally stats. Enjoy!
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:
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:
- One player was much more dominant on serve (think 4 or 5-point games instead of 6+)
- 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%