You’ve seen my pre-tournament odds for Wimbledon men and women. As more matches go in the books, the numbers change. To keep track, these pages are generated several times per day:
Enjoy!
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.
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
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.