Monthly Archives: May 2012

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

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

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

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Filed under Forecasting, French Open

The Official JRank Reference

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.

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Filed under Forecasting, Reference

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%

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Filed under Forecasting, French Open, WTA

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%

 

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Filed under Forecasting, French Open

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.

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Filed under French Open, Research

How Does the Blue Clay Play?

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

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

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

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

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

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

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

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Double London Doubles Qualifying

The top doubles players tend to stick with their partners for a long time–at the very least, through an entire season. But for all sorts of reasons, even the best players sometimes switch partners more quickly. Marc Lopez plays with Rafael Nadal sometimes, but more frequently this year with Marcel Granollers. Radek Stepanek and Leander Paes, this year’s top teams, have both played events in 2012 with others. Sometimes pairings are simply a matter of convenience.

All this can lead to some oddities in the doubles race to London. Right now, Lopez and Granollers are 8th in the race, in position to qualify for the tour finals. In 10th are Lopez and Nadal! Sure, that’s on the strength of a single tournament win, but a couple more titles at Masters events and Lopez/Nadal would find themselves in the running for the tour finals as well.

Marc Lopez’s double-dipping is unusually successful, but not that unusual. Sam Querrey is in the top 32 with two different partners (John Isner and James Blake) and Paes twice appears in the top 35 (with Stepanek and Janko Tipsarevic).

It’s early still in the 2012 race, and by September, these oddities may have faded away. But it’s tempting to wonder: Could a player qualify for London with two different partners?

Let’s take the case of Lopez. With two more Masters-or-better titles, he and Nadal would have at least 3,000 points. That would’ve been enough to qualify them for the finals last year. And if Lopez plays every other event with Granollers and puts up a decent showing in the remaining slams, it’s very possible that the Lopez/Granollers team would reach 3,000 points as well.

That was easy! If you’re a world-class doubles player, take a bit of good luck on the court (wins!) and a bit of bad luck off the court (partner injuries or drama), and you’ve got yourself doubly qualified.

To consider another example: Paes and Stepanek are, on the strength of their hot start, already qualified in all but name. If Stepanek got hurt, or went on a tear in singles and had to cut back on his doubles schedule, Paes would have more than half the season left to start over with a new partner. (Or play more with Tipsarevic, with whom he has already won a few matches.) The new pairing would have to gel quickly, but if it did, you’ve got yourself two Paes’s in London.

Now, if anybody started televising some doubles, the race would get really exciting.

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