Category: Forecasting

Australian Open Men’s R32 Projections

The top seven seeds are still alive, so in the big picture, not much has changed since I posted pre-tournament odds.   The big names have all seen their chances of winning creep up a little bit,  largely because they’ve gotten past the dangers of the first two rounds.  Some upsets elsewhere in the draw have helped, as well.

The biggest winner on that score is Juan Martin del Potro, whose chances have jumped from 2.6% to 4.2%, as he’s been granted what should be two easy matches before a quarterfinal showdown with Roger Federer.

Player                       R16     QF     SF        W
(1)Novak Djokovic          91.9%  76.3%  59.2%    26.7%
Nicolas Mahut               8.1%   2.6%   0.7%     0.0%
(23)Milos Raonic           79.8%  19.3%   9.0%     1.1%
(WC)Lleyton Hewitt         20.2%   1.8%   0.3%     0.0%
(9)Janko Tipsarevic        54.6%  28.4%   8.9%     1.3%
(17)Richard Gasquet        45.4%  21.7%   5.9%     0.7%
(27)Juan Ignacio Chela     13.9%   2.3%   0.2%     0.0%
(5)David Ferrer            86.1%  47.7%  15.8%     2.7%  

Player                       R16     QF     SF        W
(4)Andy Murray             81.4%  55.6%  34.5%    10.4%
Michael Llodra             18.6%   6.6%   2.0%     0.1%
Mikhail Kukushkin          23.8%   4.8%   1.2%     0.0%
(14)Gael Monfils           76.2%  33.0%  16.3%     2.9%
Julien Benneteau           31.6%   8.8%   2.3%     0.1%
(24)Kei Nishikori          68.4%  29.6%  12.4%     1.8%
Frederico Gil               8.8%   1.5%   0.1%     0.0%
(6)Jo-Wilfried Tsonga      91.2%  60.1%  31.3%     7.3%  

Player                       R16     QF     SF        W
Alejandro Falla            35.9%   9.6%   1.9%     0.1%
Philipp Kohlschreiber      64.1%  24.7%   7.3%     0.5%
Yen-Hsun Lu                21.6%   9.1%   1.8%     0.1%
(11)Juan Martin Del Potro  78.4%  56.5%  26.4%     4.6%
(13)Alexandr Dolgopolov    49.2%  16.3%   8.3%     0.9%
Bernard Tomic              50.8%  17.2%   8.7%     1.0%
Ivo Karlovic               18.4%   7.0%   2.8%     0.2%
(3)Roger Federer           81.6%  59.5%  42.8%    13.2%  

Player                       R16     QF     SF        W
(7)Tomas Berdych           71.3%  44.9%  20.9%     4.4%
(30)Kevin Anderson         28.7%  12.0%   3.3%     0.2%
(21)Stanislas Wawrinka     64.6%  31.0%  12.3%     1.8%
(10)Nicolas Almagro        35.4%  12.2%   3.3%     0.2%
(16)John Isner             55.1%  17.1%   7.7%     0.9%
(18)Feliciano Lopez        44.9%  12.4%   5.0%     0.5%
(q)Lukas Lacko             13.2%   4.4%   1.2%     0.0%
(2)Rafael Nadal            86.8%  66.1%  46.4%    16.1%

Australian Open Men’s Draw Predictions

A lot has changed in the last few months of men’s tennis, yet as far as one computer is concerned, all the important things have remained the same.

Novak Djokovic and Rafael Nadal limped to the finish line of the 2011 season, while Andy Murray didn’t even get through the round robin in London.  All the while, Roger Federer re-asserted his dominance, winning the last three events of the year.  For all that, my rankings can’t ignore Djokovic’s dominance over the last year, nor Nadal’s before that.

My simulation of the Australian Open draw gives a heavy edge to Novak Djokovic, with a 25.8% chance of winning it all.  Nadal is next at 15.1%, followed by Federer at 12.7% and Murray at 9.3%.  Jo-Wilfried Tsonga, at 5.3%, is the only other man with better than a one in twenty chance of winning the slam.

Djokovic also has the most lopsided first-round match.  Paolo Lorenzi just barely snuck into the main draw, and he’ll likely be on a flight home in a couple of days.  Almost as likely to make first-round exits are Aussie wildcards Benjamin Mitchell and James Duckworth, who play John Isner and Jurgen Zopp, respectively.  Yes, Duckworth’s resume is so sparse that my system gives him less than a one-in-ten chance of beating the Estonian qualifier.  I wouldn’t recommend running to your bookie with that one–it says more about Duckworth’s lack of high-level match play than it does about Zopp’s potential for dominance.

As always, the first round promises to full of tight contests, even while the seeds coast through.

  • My system gives Cedrik-Marcel Stebe a 70% chance of beating Lleyton Hewitt; that’s a reflection of how the Aussie’s health has hindered him for so long.
  • Speaking of Aussies, Bernard Tomic is set to upset 27th seed Fernando Verdasco.  My numbers put the youngster at 52.5%.
  • Ivo Karlovic is always a threat, and my system gives him a 49% chance of beating Jurgen Melzer.
  • Juan Ignacio Chela keeps earning seeds, but I keep betting against him.  This time, my system gives Michael Russell a 55% chance of scoring a first-round upset over the clay-court specialist.
  • The opening match between Michael Llodra and Ernests Gulbis is more likely to be lost than won.  I predict Llodra will lose it.
  • Thomaz Bellucci and Dudi Sela are as close as they come.  My system gives the Israeli a tiny edge of 50.4% to 49.6%.
  • Even closer are Alejandro Falla and Fabio Fognini, at 49.9%/50.1%.  Falla is dangerous, and Fognini is a threat to himself.
  • Philipp Kohlschreiber is always a dark horse, and my system suggests he’s nearly even (46.9%) with first-round opponent Juan Monaco.
  • British qualifier James Ward lucked into a friendly draw against Blaz Kavcic.   I give the Brit a 48.4% chance.

The results of my full draw simulation are below.

(If you’re interested in my ranking system, click here.  Once I have rankings, I use the draw to “play” the tournament 1,000,000 times.)

Player                       R64    R32    R16        W  
(1)Novak Djokovic          96.0%  85.6%  77.8%    25.8%  
Paolo Lorenzi               4.1%   1.1%   0.4%     0.0%  
Santiago Giraldo           80.3%  12.2%   7.4%     0.1%  
(q)Matteo Viola            19.7%   1.0%   0.3%     0.0%  
(WC)Tatsuma Ito            63.7%  25.8%   3.1%     0.0%  
Potito Starace             36.3%  10.4%   0.8%     0.0%  
Nicolas Mahut              39.3%  22.8%   3.0%     0.0%  
(29)Radek Stepanek         60.7%  41.1%   7.4%     0.1%  

Player                       R64    R32    R16        W  
(23)Milos Raonic           90.5%  60.8%  35.8%     0.9%  
Filippo Volandri            9.5%   1.7%   0.3%     0.0%  
Lukas Rosol                39.3%  12.5%   4.4%     0.0%  
Philipp Petzschner         60.7%  25.0%  11.3%     0.1%  
Cedrik-Marcel Stebe        70.3%  30.7%  13.9%     0.1%  
(WC)Lleyton Hewitt         29.7%   7.4%   2.0%     0.0%  
Robin Haase                35.6%  18.9%   8.0%     0.0%  
(15)Andy Roddick           64.4%  43.0%  24.3%     0.6%  

Player                       R64    R32    R16        W  
(9)Janko Tipsarevic        68.8%  50.7%  30.5%     1.1%  
Dmitry Tursunov            31.2%  17.6%   7.3%     0.0%  
(q)Jurgen Zopp             94.0%  31.4%  11.6%     0.0%  
(WC)James Duckworth         6.0%   0.2%   0.0%     0.0%  
Mikhail Youzhny            64.6%  31.6%  16.4%     0.3%  
(q)Andrey Golubev          35.4%  12.2%   4.5%     0.0%  
Andreas Seppi              39.6%  19.6%   9.2%     0.1%  
(17)Richard Gasquet        60.4%  36.6%  20.5%     0.5%  

Player                       R64    R32    R16        W  
(27)Juan Ignacio Chela     44.9%  20.6%   4.4%     0.0%  
Michael Russell            55.1%  27.7%   6.7%     0.0%  
Igor Kunitsyn              61.0%  34.0%   9.3%     0.0%  
Pablo Andujar              39.0%  17.6%   3.3%     0.0%  
Matthias Bachinger         53.5%  16.6%  10.6%     0.0%  
Ryan Sweeting              46.5%  13.2%   8.0%     0.0%  
Rui Machado                10.2%   2.6%   1.0%     0.0%  
(5)David Ferrer            89.8%  67.7%  56.8%     2.6%  

Player                       R64    R32    R16        W  
(4)Andy Murray             82.4%  71.1%  57.3%     9.2%  
Ryan Harrison              17.6%  10.1%   4.9%     0.0%  
Xavier Malisse             58.9%  12.4%   5.5%     0.0%  
Edouard Roger-Vasselin     41.1%   6.5%   2.3%     0.0%  
Michael Llodra             42.0%  24.4%   7.4%     0.1%  
Ernests Gulbis             58.0%  38.2%  13.8%     0.3%  
Daniel Gimeno-Traver       41.4%  13.6%   2.8%     0.0%  
(32)Alex Bogomolov Jr      58.6%  23.7%   6.1%     0.0%  

Player                       R64    R32    R16        W  
(19)Viktor Troicki         73.7%  43.4%  19.4%     0.3%  
Juan Carlos Ferrero        26.3%   8.9%   2.2%     0.0%  
Guillermo Garcia-Lopez     55.6%  27.8%  10.8%     0.1%  
Mikhail Kukushkin          44.4%  19.9%   6.8%     0.0%  
Thomaz Bellucci            49.6%  15.3%   6.9%     0.0%  
Dudi Sela                  50.4%  15.3%   6.8%     0.0%  
(WC)Marinko Matosevic      16.9%   6.5%   2.2%     0.0%  
(14)Gael Monfils           83.1%  62.9%  45.0%     2.6%  

Player                       R64    R32    R16        W  
(12)Gilles Simon           83.5%  57.3%  31.7%     1.0%  
(q)Danai Udomchoke         16.5%   5.0%   1.1%     0.0%  
Julien Benneteau           66.3%  28.0%  12.0%     0.1%  
Karol Beck                 33.7%   9.7%   2.8%     0.0%  
Joao Souza                 31.2%   5.6%   1.2%     0.0%  
Matthew Ebden              68.8%  21.3%   7.8%     0.0%  
Stephane Robert            15.9%   6.8%   1.7%     0.0%  
(24)Kei Nishikori          84.1%  66.3%  41.7%     1.9%  

Player                       R64    R32    R16        W  
(26)Marcel Granollers      75.3%  51.0%  23.6%     0.8%  
(WC)Jesse Levine           24.7%  10.3%   2.6%     0.0%  
Frederico Gil              23.7%   4.9%   0.8%     0.0%  
Ivan Dodig                 76.3%  33.8%  12.2%     0.1%  
(q)Roberto Bautista-Agut   57.2%  11.1%   3.4%     0.0%  
Ricardo Mello              42.8%   6.7%   1.8%     0.0%  
Denis Istomin              19.6%  12.3%   4.9%     0.0%  
(6)Jo-Wilfried Tsonga      80.4%  70.0%  50.6%     5.3%  

Player                       R64    R32    R16        W  
(8)Mardy Fish              77.6%  60.5%  42.1%     2.3%  
Gilles Muller              22.4%  11.1%   4.5%     0.0%  
Alejandro Falla            49.9%  14.1%   5.6%     0.0%  
Fabio Fognini              50.1%  14.3%   5.9%     0.0%  
Albert Montanes            62.9%  19.0%   5.5%     0.0%  
Pere Riba                  37.1%   7.5%   1.5%     0.0%  
Philipp Kohlschreiber      46.9%  33.9%  15.5%     0.2%  
(25)Juan Monaco            53.1%  39.6%  19.4%     0.3%  

Player                       R64    R32    R16        W  
(20)Florian Mayer          71.3%  51.0%  27.1%     0.9%  
Yen-Hsun Lu                28.7%  14.7%   4.9%     0.0%  
(q)Florent Serra           31.9%   7.6%   1.7%     0.0%  
Steve Darcis               68.1%  26.6%   9.8%     0.1%  
(q)James Ward              48.4%  11.6%   4.0%     0.0%  
Blaz Kavcic                51.6%  13.1%   4.8%     0.0%  
Adrian Mannarino           27.2%  16.7%   7.6%     0.1%  
(11)Juan Martin Del Potro  72.8%  58.7%  40.1%     2.6%  

Player                       R64    R32    R16        W  
(13)Alexandr Dolgopolov    75.2%  55.5%  29.3%     0.7%  
(WC)Greg Jones             24.8%  12.3%   3.5%     0.0%  
Tobias Kamke               68.7%  25.4%   8.5%     0.0%  
Victor Hanescu             31.3%   6.8%   1.3%     0.0%  
(WC)Kenny De Schepper      18.2%   2.9%   0.7%     0.0%  
Sam Querrey                81.8%  37.5%  21.0%     0.4%  
Bernard Tomic              52.5%  31.9%  19.3%     0.5%  
(22)Fernando Verdasco      47.5%  27.7%  16.3%     0.4%  

Player                       R64    R32    R16        W  
(31)Jurgen Melzer          51.0%  38.6%  10.8%     0.1%  
Ivo Karlovic               49.0%  36.5%  10.1%     0.1%  
Carlos Berlocq             36.9%   6.9%   0.7%     0.0%  
(q)Jesse Huta Galung       63.1%  18.0%   2.8%     0.0%  
Eric Prodon                19.8%   1.1%   0.2%     0.0%  
Andreas Beck               80.2%  14.0%   6.4%     0.0%  
(q)Alexander Kudryavtsev    9.6%   4.6%   1.7%     0.0%  
(3)Roger Federer           90.4%  80.3%  67.4%    12.7%  

Player                       R64    R32    R16        W  
(7)Tomas Berdych           87.9%  72.2%  54.5%     4.3%  
Albert Ramos               12.1%   4.8%   1.4%     0.0%  
Olivier Rochus             65.4%  17.1%   7.7%     0.0%  
(q)Bjorn Phau              34.6%   5.8%   1.8%     0.0%  
Sergiy Stakhovsky          65.3%  34.3%  12.4%     0.1%  
(q)Illya Marchenko         34.7%  13.0%   3.2%     0.0%  
(q)Frederik Nielsen        27.4%   9.7%   2.1%     0.0%  
(30)Kevin Anderson         72.6%  43.1%  17.0%     0.2%  

Player                       R64    R32    R16        W  
(21)Stanislas Wawrinka     76.0%  45.9%  31.1%     1.2%  
Benoit Paire               24.0%   8.0%   3.2%     0.0%  
Marcos Baghdatis           71.9%  37.4%  23.8%     0.7%  
Benjamin Becker            28.1%   8.6%   3.4%     0.0%  
Jeremy Chardy              54.8%  29.2%  11.9%     0.1%  
Grigor Dimitrov            45.2%  21.7%   7.8%     0.0%  
Lukasz Kubot               40.4%  17.4%   5.6%     0.0%  
(10)Nicolas Almagro        59.6%  31.6%  13.1%     0.1%  

Player                       R64    R32    R16        W  
(16)John Isner             94.2%  53.0%  32.2%     0.8%  
(WC)Benjamin Mitchell       5.8%   0.5%   0.0%     0.0%  
Jarkko Nieminen            33.2%  11.9%   5.2%     0.0%  
David Nalbandian           66.8%  34.6%  20.7%     0.5%  
Flavio Cipolla             36.4%  14.4%   4.4%     0.0%  
Nikolay Davydenko          63.6%  33.6%  14.9%     0.1%  
Leonardo Mayer             24.8%   8.0%   1.9%     0.0%  
(18)Feliciano Lopez        75.2%  44.1%  20.7%     0.3%  

Player                       R64    R32    R16        W  
(28)Ivan Ljubicic          62.6%  37.8%  10.7%     0.2%  
(q)Lukas Lacko             37.4%  18.1%   3.7%     0.0%  
(q)Peter Gojowczyk         19.2%   3.7%   0.4%     0.0%  
Donald Young               80.8%  40.4%  10.3%     0.1%  
Tommy Haas                 34.1%   1.8%   0.3%     0.0%  
(q)Denis Kudla             65.9%   6.1%   1.6%     0.0%  
(q)Alex Kuznetsov          10.1%   6.8%   2.4%     0.0%  
(2)Rafael Nadal            89.9%  85.3%  70.7%    15.1%

Living Up to Your Seeding

Listen to the commentary during tennis tournaments and you’ll hear a lot about “living up” or “playing up” to one’s seed.  In other words, a seed implies a certain level of performance. If you’re #10, you should reach the round of 16, but it would take an upset to get to the quarterfinals.

Of course, most players aren’t that consistent.  Sometimes they beat expectations (even Igor Kunitsyn won a tournament) and sometimes they crash out early (hello, Andy Murray!).  While guys like David Ferrer seem to steer a middle course, each player’s ranking is really just a weighted average of the tournaments where they ruled the world and the events where they shouldn’t have gotten out of bed.

And the more you think about it, the more the notion of “living up to your seeding” falls apart.  In order for the top seed at a tournament to meet expectations, he has to win.  That happens considerably less than half the time.  For the second seed to go home happy, he needs to reach the final.  But with rare exceptions, someone who lost in the final every week would quickly amass enough ranking points to be #1.  So at least at the top, we shouldn’t expect that level of consistency.  Also, the whole idea sets the same expectations for the 9th seed as the 16th, the 17th seed as the 32nd.  We can do better.

I looked at the last 20 years of slam results and figured out the average result for every seed.  In that time span, the top seed has won 5.0 matches per slam–on average, then, he has lost in the semifinals.  That number has increased since the majors started seeding 32 players in 2002: In the last 10 years, the top seed has won 5.3 matches per slam, as he has generally coasted through the first two rounds.

Here’s a look at how each seed has done over the last 20 years.  After the top few guys, no one should be expected to reach the quarters–certainly not the #8 seed!

Seed       Wins            
1          5.0   SF        
2          4.2   QF+       
3          3.7   QF-       
4          3.4   R16+      

5          2.7   R16-      
6          2.9   R16-      
7          2.5   R32/R16   
8          2.1   R32+      

9          2.5   R32/R16   
10         2.7   R16-      
11         2.2   R32+      
12         2.6   R16-      

13         2.1   R32+      
14         2.2   R32+      
15         2.1   R32+      
16         1.6   R64/R32   

17-32      1.6   R64/R32   
UNR 92-01  0.7   R64-      
UNR 02-11  0.6   R128/R64

A more sophisticated way of looking at this is with probabilities.  Sure, the smart money is on the top seed winning five matches, but beyond knowing that he wins the tournament between 35 and 40 percent of the time, what are the odds that he reaches the final?  Crashes out early?

Here are those odds for the same sets of players:

Seed         R64    R32    R16     QF     SF      F      W  
1          97.3%  90.5%  83.8%  75.7%  62.2%  48.6%  36.5%  
2          88.5%  78.2%  70.5%  60.3%  51.3%  34.6%  24.4%  
3          93.5%  80.5%  70.1%  57.1%  36.4%  19.5%   5.2%  
4          84.4%  75.3%  64.9%  55.8%  39.0%  14.3%   7.8%  

5          84.2%  71.1%  47.4%  36.8%  15.8%   7.9%   2.6%  
6          84.2%  67.1%  56.6%  38.2%  21.1%  13.2%   7.9%  
7          81.3%  69.3%  52.0%  32.0%  16.0%   4.0%   0.0%  
8          80.3%  61.8%  47.4%  22.4%   2.6%   1.3%   0.0%  

9          86.3%  70.0%  53.8%  28.8%  13.8%   5.0%   0.0%  
10         88.2%  69.7%  52.6%  31.6%  10.5%   5.3%   2.6%  
11         93.2%  63.0%  34.2%  15.1%   4.1%   1.4%   0.0%  
12         84.8%  70.9%  51.9%  34.2%  19.0%   5.1%   2.5%  

13         79.5%  61.5%  48.7%  12.8%   7.7%   3.8%   2.6%  
14         82.7%  60.0%  42.7%  18.7%   9.3%   2.7%   0.0%  
15         81.8%  67.5%  41.6%  15.6%   7.8%   3.9%   0.0%  
16         72.7%  44.2%  28.6%   7.8%   5.2%   2.6%   1.3%  

17-32      72.5%  51.8%  19.7%   8.2%   2.2%   0.9%   0.4%  
UNR 92-01  42.6%  15.8%   5.7%   1.9%   0.6%   0.2%   0.0%  
UNR 02-11  40.1%  12.8%   4.3%   1.2%   0.4%   0.2%   0.0%

The same sample of no more than 80 slams means that these numbers don’t give us a smooth curve, but they still provide a pretty good idea.  In fact, they look awfully similar to my pre-tournament slam predictions, with the exception of the big gap between the top two seeds and the rest of the field.

Grand Slam Forecasting for Dummies

It’s one thing to predict a winner–it’s another thing to quantify how likely a player is to become that winner.

In most tennis tournaments, it’s not hard to pick a favorite.  For most of the last year, it was Novak Djokovic, no matter the surface or who he might face.  Before that, it was Federer on hard courts, Nadal on clay courts.  While every one likes to identify a dark horse, there’s rarely much debate at the top.

Given that agreement, though, what odds would you have placed on Novak Djokovic winning Wimbledon?  Or the French?  Or an in-form Federer winning the tour finals over an injured Djokovic and a tired Nadal?  Usually, my numbers spit out something between 20 and 30 percent–in theory, even the best player in the tournament has a better than two-thirds chance of going home a loser.

Intuitively, this is difficult to believe.  Djokovic seemed so dominant for much of the year that his slam victories felt like foregone conclusions.  Anyone who watched Novak on a good day found it impossible to imagine anyone outplaying him.  When Carl Bialik wrote a column asking whether Djokovic could keep up his dominance for the entire season, most responses were some variation of “What are you, stupid? Numbers are irrelevant when someone is so good.”

But, all good things must come to end, and a combination of injuries and good opponents proved that even Djokovic is human.

That said, Djokovic’s dominance–and Nadal’s before him, and Federer’s before him–raises questions about forecasting tennis matches.   The questions are complicated, but rest easy: today’s attempt at an answer will be simple.

Do the rules apply to the very best?

My ranking and forecasting system starts by assigning a number to every player, not unlike ATP ranking points.  To keep things simple, let’s use ranking points.  If we want to predict the outcome of, say, Mardy Fish against Feliciano Lopez, we take their point totals (2965 and 1755) and divide one by the sum of the others: 2965/(2965+1755) = 62.8%.  (It’s a little more complicated than that, but not much.)  Setting aside concerns like home court advantage and surface, that sounds about right to me.

Do the same with Djokovic and Lopez, and you get 88.6%.  Work the numbers with Djokovic and world #100 Michael Berrer, and you get 96.0%.  That’s pretty dominant, suggesting that Berrer would win only 1 in 25 matchups, but wait a minute–we’re saying Berrer’s going to beat Djokovic, ever?

And therein lies the problem.  The formulas I use to generate points and generate predictions are reasonably accurate, tested against years of ATP results.  And in the aggregate, individual match percentages pass the smell test.  But at the extremes, the numbers seem questionable.

And it is at the extremes where the exact percentages matter the most.  Consider my pre-tournament predictions for Wimbledon this year.  While Nadal was the top seed, I picked Djokovic as the favorite, giving him a 21.6% chance of winning.  But look at those first few rounds: I gave him only an 87% chance of getting past Jeremy Chardy (Jeremy Chardy!) in the first round, then only an 88% chance of beating Kevin Anderson or Ilya Marchenko, then only an 85% chance of winning against (probably) Marcos Baghdatis.

Only the last of those three numbers is plausible.  And when combined, they meant that I gave Djokovic less than a 65% chance of reaching the round of 16.  With all due respect to myself, that was almost as ridiculous then as it it sounds now.

It’s those early-round numbers that result in such minute chances that the favorite will win the tournament.  Even if we give a player a 90% chance of winning all his matches, he’ll still only win the seven consecutive matches required for a grand slam 48% of the time.  Lower it to 80%, and we’re down to 21% for the tournament.  Since the odds of winning a semifinal match against the likes of Murray, Federer, or Nadal is probably much lower, it seems that early round odds should be much more favorable.

To summarize, one of two things is going on here.  Either (1) my numbers underestimate the likelihood that the pre-tournament favorite wins a grand slam; or (2) our intuition overestimates the likelihood that the favorite takes home the trophy.

Forecasting for dummies

One way to pick between the two is to look at the recent past.  Are pre-tournament favorites winning more or less than expected?

For now, let’s set aside the question of the likelihood that Djokovic beats Chardy or Marchenko, and look only at winning the tournament.  We’re going to make two major assumptions here: (1) it’s possible to identify the pre-tournament favorite years later, and (2) favorites are generally created equal–Djokovic towers over his competitors to the same degree that Courier, or Lendl, or Sampras, or Federer towered over his.  As usual, both of these assumptions probably aren’t true, but they aren’t so hideously wrong that they’ll stop us from reaching some worthwhile conclusions.

There are three easy ways of picking the pre-tournament favorite for a grand slam: using (a) the winner of the last slam; (b) the defending champion, and (c) the top seed–almost always the world #1.  The top seed is probably best, while the defending champion might identify a player who is particularly good on the surface, and the winner of the last slam might pick out someone who is riding a hot streak.

The last 21 years (back to 1991, inclusive), give us 84 slams to work with.  Our sample is a bit smaller than that, because occasionally the winner of the last slam or the defending champion did not play, and on three occasions, the top seed pulled out before the tournament began.  Here is how the favorites did:

  • Of the 75 players who had won the previous slam, 18 (24%) won the tournament.
  • Of the 76 defending champions, 26 (34%) won the tournament.
  • Of the 81 top seeds, 29 (36%) won the tournament.  If we exclude the French (where the top seed is often #1 on the basis of hard court performance), we get a more dramatic result here–26 of 60 (43.3%) won the tournament.

All of these measures are much higher than the 21.6% shot I gave Djokovic at Wimbledon.  And most are higher than the 27-28% chances I gave him at the French and US Open.  The 43.3% likelihood that the top seed wins a hard-court slam (thank you, Pete and Roger!) suggests that a more sophisticated measure of identifying the favorite might allow us to predict slam champions with, say, 40% accuracy.

40% is considerably higher than my models are spitting out right now, but I suspect it is much lower than many fans imagine for their favorite.  It suggests that, at the extremes, my predictions aren’t quite one-sided enough.  It might take Michael Berrer more than 25 chances before he finally catches Djokovic on a bad day.

US Open Women’s Draw Predictions

Serena Williams dominates my most recent WTA hard court rankings, so it’s no surprise that she’s favored to win the U.S. Open.  As was the case before Wimbledon, it’s remarkable to see how chaotic the women’s field is.  While Novak Djokovic has a 28% chance of winning the men’s event, Serena is the only woman in double digits, at 14.2%.

Because of Serena’s low seeding at #28, a decisive match may take place in the first week.  Assuming some easy wins for both Williams and Victoria Azarenka, the two ladies will face off in the third round.  My algorithm gives the American a 59% chance of winning that match, meaning it could be the toughest test she faces in the entire tournament.

Behind Serena, Carolina Wozniacki has a 9.8% chance of winning the U.S. Open, followed by Maria Sharapova at 9.2%.  Next up are Petra Kvitova at 8.0% and Vera Zvonareva at 7.9%.  An amazing 21 women (compared to 13 men) have at least a 1% chance of going home a champion.  These include the unseeded Venus Williams (1.8%) and the 32nd seeded Maria Jose Martinez Sanchez (1.0%, or 0.25% per name).

The conspiracy-minded among you might note that top seeds Wozniacki and Zvonareva have the most favorable first-round odds, despite my system ranking them only 3rd and 7th on hard courts.  Their opening opponents, Nuria Llagostera Vives and Stephanie Foretz Gacon, are the 16th and 23rd weakest players in the draw, according to the current WTA rankings.  (My system isn’t as reliable that far down the WTA list.)  Caro and Vera are the only two players with a better-than-90% chance of winning their openers, though both Sharapova, Marion Bartoli, and Andrea Petkovic are at an even 90%.

Here are a few interesting first-rounders.  In each of these, my system gives neither player a better than 55% chance of advancing, with the favorite in bold:

  • Greta Arn vs Vania King
  • Eleni Daniilidou vs Michaella Krajicek
  • Anne Keothavong vs Chanelle Scheepers
  • Francesca Schiavone vs Galina Voskoboeva (the 7th seed doesn’t have good results on hard courts)
  • Alla Kudryavtseva vs Anastasia Rodionova
  • Misaki Doi vs Laura Pous-Tio
  • Melania Oudin vs Romani Oprandi
  • Anastasija Sevastova vs Vera Dushevina (nearly 50/50)
  • Maria Kirilenko vs Ekaterina Makarova (Kirilenko is another vulnerable seed)
  • Coco Vandeweghe vs Alberta Brianti
Here’s the full set of predictions: each player, every round.  It will updated throughout the tournament.

US Open Men’s Draw Predictions

Get ready for a shock: I’m forecasting Novak Djokovic as the winner of this year’s U.S. Open.  I give Djokovic a 27.8% chance of winning the tournament–a higher probability than I gave him at Wimbledon.

There’s a marked difference between Novak and the rest of the pack, in part because Juan Martin del Potro could wreak havoc with the bottom half of the draw.  I give Rafael Nadal a 14.6% chance, Andy Murray 9.2%, and Delpo 6.6%.

Federer comes in fourth behind Murray, at 8.9%.  Making his road tricky is a likely quarterfinal matchup with either Jo-Wilfried Tsonga or Mardy Fish.  Fish does better in my hard-court rankings than on the ATP computer, and is sixth-most likely to win the tournament at 4.2%.  Tsonga comes in 7th at 3.8%, as he shines on hard courts.  Also, my algorithm takes into account Tsonga’s wins over Fed.

Seeded Americans Andy Roddick and John Isner do better than their rankings would suggest, in large part due to their hard-court prowess.  Roddick has a 2.1% chance, and Isner 0.2%.  The overall chance that an American wins the event is 6.6%–just a tick above the combined probability of Fish or Roddick winning, and equal to Delpo’s shot.

The unseeded player my system favors is Nikolay Davydenko, at 0.7%.  Recent disasters aside, he is one of the few players who has proven he can beat the best players in the game.  As I recently wrote, his inconsistency may actually be a good thing.

There are several first-rounders that figure to be extremely tight matches.  Here are all the opening matchups where the favorite (in bold) has less than a 55% chance of getting through to the second round:

  • Granollers vs Malisse
  • Kukushkin vs Montanes
  • Bellucci vs Sela
  • Kohlschreiber vs Stepanek (almost dead even)
  • Bubka vs Haider-Maurer (also nearly even)
  • Dancevic vs Ilhan (two qualifiers)
  • Baghdatis vs Isner (maybe that would change if I re-ran my rankings through Winston-Salem)
  • Young vs Lacko (Kubot pulled out, making both Donald and Lacko lucky)
  • Matosevic vs Chela (actually 58/42, Chela being the least-favored seed)
  • Rosol vs Pospisil (another 50/50, I’d probably bet on the young Canadian)
  • Istomin vs Sweeting
  • Roger-Vasselin vs Muller
For the full breakdown, I’ve published the chart here.  That page will update automatically throughout the tournament.

ATP Cincinnati Predictions

If last week’s tournament in Montreal taught us anything, it’s that predicting the outcome of ATP matches is a fool’s errand.  With that in mind, let’s see what the draw has in store for us in Cinci!

The draw this week is what the Master’s series is all about.  With the exception of a couple of late withdrawals (Tsonga?) that may yet come down the pike, nearly every top player in the men’s game is in Cincinnati.  Andy Roddick is trying to return from injury; David Ferrer makes his summer hard-court debut, and we’re already set for a Federer/del Potro showdown in the second round.

Del Potro’s mere presence makes every tournament a little more interesting.  He’s laid a couple of eggs recently, losing to Gulbis and Cilic, but he tore up the spring hard court circuit and lost only to the best of the best on clay.  My ranking system still gives him a lot of respect, keeping him within the top five, which makes Federer’s route to the semifinals (heck, the third round!) look particularly challenging.

Djokovic (who, once again, is in Fed’s half) could face a slew of Americans on their home turf.  His probable second-round opponent is Ryan Harrison, who I favor heavily over Juan Ignacio Chela.  After that, it’s easy to see John Isner in the third round, and possibly Andy Roddick in the quarters.  It’s theoretically possible, but a little less likely that another American, James Blake, will make it through the semis to be Novak’s opponent in that round.

Here is my full projection.  For purity’s sake, it doesn’t reflect the results of today’s two matches, in which Delpo and Blake both advanced.

Player                        R32    R16     QF         W  
(1)Novak Djokovic          100.0%  90.1%  74.4%    29.61%  
(WC)Ryan Harrison           71.5%   8.6%   3.2%     0.07%  
Juan Ignacio Chela          28.5%   1.3%   0.3%     0.00%  
(q)Radek Stepanek           43.6%  18.2%   3.0%     0.09%  
John Isner                  56.4%  24.7%   5.0%     0.24%  
Andrey Golubev              23.2%   8.4%   1.0%     0.02%  
(16)Stanislas Wawrinka      76.8%  48.7%  13.0%     1.41%  

Player                        R32    R16     QF         W  
(11)Andy Roddick            63.7%  43.3%  23.0%     1.34%  
Philipp Kohlschreiber       36.3%  20.8%   9.1%     0.24%  
Juan Carlos Ferrero         23.9%   4.3%   0.9%     0.00%  
Feliciano Lopez             76.1%  31.6%  13.3%     0.29%  
Ivan Dodig                  39.4%  10.9%   3.9%     0.05%  
(q)Ernests Gulbis           60.6%  24.3%  11.6%     0.32%  
(6)Gael Monfils            100.0%  64.9%  38.2%     2.22%  

Player                        R32    R16     QF         W  
(3)Roger Federer           100.0%  58.9%  44.5%    10.38%  
Juan Martin del Potro       82.9%  38.8%  27.9%     5.00%  
Andreas Seppi               17.1%   2.4%   0.8%     0.01%  
(WC)James Blake             23.3%   8.1%   1.1%     0.01%  
Marcos Baghdatis            76.7%  46.2%  15.2%     1.02%  
Fabio Fognini               26.1%   7.6%   0.9%     0.01%  
(14)Viktor Troicki          73.9%  38.1%   9.6%     0.40%  

Player                        R32    R16     QF         W  
(9)Nicolas Almagro          62.0%  32.1%  14.0%     0.25%  
Albert Montanes             38.0%  13.8%   4.0%     0.03%  
Ivo Karlovic                32.6%  14.0%   4.6%     0.04%  
Florian Mayer               67.4%  40.2%  18.2%     0.56%  
Tommy Haas                  13.2%   0.8%   0.1%     0.00%  
Juan Monaco                 86.8%  28.8%  13.9%     0.21%  
(8)Tomas Berdych           100.0%  70.4%  45.3%     2.43%  

Player                        R32    R16     QF         W  
(5)David Ferrer            100.0%  75.5%  44.7%     2.52%  
(q)Marsel Ilhan             38.3%   7.5%   1.9%     0.00%  
(WC)Grigor Dimitrov         61.7%  17.0%   6.0%     0.05%  
Janko Tipsarevic            70.3%  31.7%  15.3%     0.43%  
(q)Edouard Roger-Vasselin   29.7%   8.2%   2.2%     0.01%  
Jurgen Melzer               44.2%  25.0%  12.0%     0.35%  
(10)Gilles Simon            55.8%  35.2%  17.9%     0.72%  

Player                        R32    R16     QF         W  
(15)Jo-Wilfried Tsonga      57.2%  46.4%  19.8%     2.28%  
Marin Cilic                 42.8%  32.7%  12.2%     0.85%  
(q)Alex Bogomolov Jr        57.7%  13.7%   3.0%     0.04%  
(WC)Robby Ginepri           42.3%   7.2%   1.1%     0.01%  
(q)Kei Nishikori            46.5%  10.6%   4.5%     0.14%  
David Nalbandian            53.5%  13.1%   5.9%     0.27%  
(4)Andy Murray             100.0%  76.3%  53.6%    10.62%  

Player                        R32    R16     QF         W  
(7)Mardy Fish              100.0%  63.6%  42.6%     3.59%  
Nikolay Davydenko           68.5%  28.8%  16.7%     0.99%  
Sergiy Stakhovsky           31.5%   7.6%   3.0%     0.04%  
Xavier Malisse              55.4%  21.5%   6.7%     0.11%  
Kevin Anderson              44.6%  15.4%   4.5%     0.04%  
Alexandr Dolgopolov         47.8%  29.6%  12.4%     0.39%  
(12)Richard Gasquet         52.2%  33.4%  14.0%     0.52%  

Player                        R32    R16     QF         W  
(13)Mikhail Youzhny         54.6%  28.3%   7.7%     0.41%  
Michael Llodra              45.4%  20.5%   4.9%     0.17%  
Thomaz Bellucci             36.9%  15.1%   3.1%     0.05%  
Fernando Verdasco           63.1%  36.0%  10.4%     0.61%  
Guillermo Garcia-Lopez      60.5%  13.3%   6.3%     0.19%  
(q)Julien Benneteau         39.5%   4.9%   1.8%     0.03%  
(2)Rafael Nadal            100.0%  81.8%  65.8%    18.36%

ATP Montreal Predictions

The big boys are back in action with this week’s Masters 1000 tournament in Montreal.  I’ve updated my rankings and generated some predictions for this week’s matches.  My system doesn’t give any credit to defending champions, so Andy Murray is in a distant third, while Djokovic’s chances of winning the tournament are lessened a bit by finding himself in the same half of the bracket as Federer.

If you’re visiting for the first time, or the first time since last week, you may be interested in the variety of content I posted over the weekend:

Once you’ve caught up, enjoy my forecast for this week’s Rogers Cup, below.

Player                        R32    R16     QF         W  
(1)Novak Djokovic          100.0%  81.1%  59.3%    23.27%  
Nikolay Davydenko           78.8%  17.5%   7.9%     0.70%  
(q)Flavio Cipolla           21.2%   1.4%   0.2%     0.00%  
Andreas Seppi               30.4%   6.9%   1.0%     0.02%  
Marin Cilic                 69.6%  27.5%   7.2%     0.61%  
Jarkko Nieminen             14.9%   4.9%   0.7%     0.01%  
(16)Juan Martin Del Potro   85.1%  60.6%  23.8%     5.21%  
                                                           
Player                        R32    R16     QF         W  
(12)Viktor Troicki          81.3%  40.4%  19.1%     0.49%  
(q)Michael Yani             18.7%   3.3%   0.6%     0.00%  
Marcos Baghdatis            57.5%  34.7%  17.9%     0.78%  
John Isner                  42.5%  21.7%   9.7%     0.19%  
(q)Alex Bogomolov Jr        41.7%  10.7%   3.4%     0.02%  
Adrian Mannarino            58.3%  19.1%   7.8%     0.10%  
(5)Gael Monfils            100.0%  70.3%  41.5%     2.19%  
                                                           
Player                        R32    R16     QF         W  
(3)Roger Federer           100.0%  91.7%  63.9%    15.48%  
Juan Ignacio Chela          52.4%   4.4%   0.9%     0.00%  
(WC)Vasek Pospisil          47.6%   3.9%   0.7%     0.00%  
(WC)Bernard Tomic           67.8%  29.9%   9.1%     0.51%  
(LL)Yen Hsun Lu             32.2%   9.3%   1.6%     0.02%  
Fabio Fognini               17.3%   5.3%   0.8%     0.01%  
(13)Jo Wilfried Tsonga      82.7%  55.4%  22.9%     2.84%  
                                                           
Player                        R32    R16     QF         W  
(10)Richard Gasquet         52.9%  34.9%  19.5%     0.60%  
Florian Mayer               47.1%  30.1%  16.9%     0.51%  
Andrey Golubev              42.9%  13.9%   5.3%     0.04%  
Thomaz Bellucci             57.1%  21.1%   9.1%     0.10%  
Sergiy Stakhovsky           38.9%  16.3%   6.6%     0.06%  
Philipp Kohlschreiber       61.1%  31.5%  16.4%     0.37%  
(8)Nicolas Almagro         100.0%  52.1%  26.1%     0.46%  
                                                           
Player                        R32    R16     QF         W  
(6)Mardy Fish              100.0%  67.4%  43.5%     3.65%  
Feliciano Lopez             53.7%  16.4%   8.0%     0.16%  
(SE)Radek Stepanek          46.3%  16.1%   7.0%     0.11%  
(WC)Ernests Gulbis          78.1%  41.7%  18.7%     0.56%  
Juan Carlos Ferrero         21.9%   5.3%   0.9%     0.00%  
Michael Llodra              46.3%  23.9%   8.8%     0.20%  
(11)Mikhail Youzhny         53.7%  29.1%  13.1%     0.44%  
                                                           
Player                        R32    R16     QF         W  
(14)Stanislas Wawrinka      59.5%  48.6%  20.7%     1.84%  
David Nalbandian            40.5%  30.2%   9.1%     0.39%  
(q)Michael Russell          37.4%   6.0%   0.7%     0.00%  
Albert Montanes             62.6%  15.2%   2.5%     0.02%  
Pablo Andujar               23.1%   1.4%   0.2%     0.00%  
Kevin Anderson              76.9%  12.7%   4.6%     0.08%  
(4)Andy Murray             100.0%  86.0%  62.1%    12.44%  
                                                           
Player                        R32    R16     QF         W  
(7)Tomas Berdych           100.0%  60.8%  37.2%     2.20%  
Alexandr Dolgopolov         93.0%  38.8%  21.6%     0.78%  
(WC)Erik Chvojka             7.0%   0.4%   0.0%     0.00%  
Ivo Karlovic                38.8%  15.2%   4.7%     0.04%  
Juan Monaco                 61.2%  27.4%  10.4%     0.21%  
(q)Philipp Petzschner       34.4%  16.3%   5.5%     0.07%  
(9)Gilles Simon             65.6%  41.1%  20.7%     0.88%  
                                                           
Player                        R32    R16     QF         W  
(15)Fernando Verdasco       72.5%  44.2%  12.1%     0.65%  
(q)Tobias Kamke             27.5%   9.8%   1.4%     0.01%  
Janko Tipsarevic            70.6%  36.4%   9.8%     0.39%  
(q)Alejandro Falla          29.4%   9.7%   1.4%     0.01%  
Ivan Dodig                  44.4%   6.5%   2.7%     0.04%  
Jeremy Chardy               55.6%   9.7%   4.6%     0.13%  
(2)Rafael Nadal            100.0%  83.8%  68.0%    20.10%

Live Wimbledon Odds

In conjunction with the work I’m doing for the Wall Street Journal’s Tennis Tracker, I’m generating a lot more data than they are able to show.  So, you can now see updated odds for each player in both the men’s and women’s singles draw, updated several times per hour.  Here are the links:

You can see the pre-tournament odds here and here.