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%

Graduating From Challengers

The best players don’t take long before they show you how good they are.  Tennis fans are rightfully excited about guys like Bernard Tomic and Milos Raonic, youngsters who have already established themselves at ATP level–if they are this good at 18, or 21, imagine how good they will be.

I’m always looking for ways to quantify that promise.  In the past, I’ve focused on the rankings, noticing that nearly everyone who reached #1 had broken into the top 100 before their 19th birthday.  Another angle is to see how long a player lasts at the challenger level.

The best players seem to skip the challenger level altogether.  It’s a bit like baseball players and Triple-A: some prospects are ready for the big time, so they never play in the highest level of the minor leagues.  Roger Federer only played eight events in his challenger career, Nadal played 12, and Djokovic played 11–out of which he won three titles.  Andy Roddick also won three challenger titles in only six events at that level.

A player can only move so quickly if they gain entry to tour-level events and they take advantage of the opportunities.  Roddick won 20 matches as a wild card in 2001.  Djokovic reached the third round of both Wimbledon and the U.S. Open on his first try.  A few accomplishments like that, plus the points from a couple of challenger titles, and you’re ranked in the top 100, good enough to earn direct entry into most ATP events.  That’s essentially what happened to Milos Raonic after he reached the fourth round in Melbourne last year.

This suggests a new type of filter to separate the prospects from the wannabes.  If someone takes two years to consistently go deep at challenger events and fails to make an impact at the ATP level, they probably aren’t headed for the top 10.  But if someone gets into the top 50 or 60 with only a couple dozen challengers in their past, they just might be something special.

I investigated the challenger careers of everyone currently in the ATP top 100.  Eight of the ten guys who played the fewest challengers are (in order): Roddick, Federer, Juan Carlos Ferrero, Djokovic, Nadal, Gael Monfils, Andy Murray, and Juan Monaco.

The other two? Milos Raonic and Bernard Tomic, who played 16 and 18 challengers, respectively.  Other prospects in the same range are Kei Nishikori (22), Cedrik-Marcel Stebe (25), and Ryan Harrison (28).  While Stebe and Harrison may play a few more, they still haven’t reached the totals of Jo-Wilfried Tsonga (29), Richard Gasquet (32), or David Ferrer (34).  Nikolay Davydenko spent even longer (41 events) on the challenger tour before beginning his ascent to world #3.

More than half of the top 100 played at least 50 challengers, and that’s generally the half you don’t want to be in.  The most promising career trajectory for challenger vets is that of Janko Tipsarevic, who played 89 challengers (winning 10) before putting it all behind him.  Most of the men near him on the list (Tobias Kamke, 88; Andreas Beck, 90; Dudi Sela, 90) can only dream of doing so well.

With a few exceptions like Tipsarevic (and Monaco, who largely skipped the challenger tour but hasn’t become a consistent threat on tour), this is a filter with some potential.  It overlaps quite a bit with age–if you see a 20-year-old in the top 100, he probably hasn’t played nearly as many challengers as a 27-year-old who finally broke in.  Where “number of challengers” might trump age is when comparing players who–for reasons that may not be purely attributable to talent–started playing professionally at much different times.  John Isner, for example, has only played 20 challengers, but didn’t break into the top 100 until he was nearly 23.  His advanced age would have told us he had little potential while hiding the fact he spent years playing college tennis.  The length of his challenger tour career indicates that once he went pro, it wasn’t long before he was ready to play with the big boys.

Whichever metric (age or challenger experience) you prefer, it’s tough to get excited about someone like Alex Bogomolov Jr., who was 28 when he first cracked the top 100, after a career including 151 challengers.  Among the current top 100, only Michael Russell and Ricardo Mello have played more.  Another man with little promise is (I’m sad to say) Flavio Cipolla, 28 years old and #75 in the world.  The Italian has played 136 challengers and won only 51% of his matches in those events.

Another lesson from these numbers is that you can watch a whole lot of challenger-level matches without seeing any real prospects.  (That isn’t to say that Kenny de Schepper versus Michael Yani isn’t entertaining.  It is.)  If future top-tenners play only a handful of challenger events, your average player in a challenger is a guy whose best hope is a peek into the top 50.  Or–if you’re lucky–Janko Tipsarevic.

Milos Raonic on Defense

One of the things I enjoy about watching up-and-comers on the ATP tour is how fast they can climb the rankings.  With few points to defend, a semifinal showing at an ATP 250 can be worth several ranking places, and a young player can string together several weeks like that.

This time last year, Milos Raonic did that (and much more) in January and February.  He started the season with a ranking of 156.  By the time he got to Indian Wells, he was up to 37.  He amassed nearly 800 points in a six-week span starting in Melbourne qualifying and ending in Memphis.  That’s more than half of his current point total, even after taking the title yesterday in Chennai and returning to his career-high ranking of 25.

In other words, Milos has his work cut out for him if he’d like to stay in the top 30.  At last year’s Australian Open, he beat Michael Llodra and Dr. Mikhail Youzhny en route to the round of 16.  Making it that far will be easier this year, since he’ll be seeded, but he’s still likely to face a top-16 player in the third round.  In San Jose, he won his first title, claiming 250 points thanks mainly to his beating Fernando Verdasco on an indoor hard court.  The next week, he racked up another 300 points for reaching the final in Memphis, this time beating both Verdasco and Mardy Fish.

The main advantage Raonic has this year is his ranking.  He wasn’t seeded at a tour-level event until late March, at the Miami Masters.  He had to defeat seeded players in the second and third rounds in Melbourne, then in the first rounds of Johannesburg, San Jose, and Memphis.  In 2012, it should be much easier going in the early rounds.

At the very least, then, Raonic won’t fall too far.  If all he does is play up to his seeding, he’ll reach the third round in Melbourne, then the quarters or semis in San Jose and Memphis.  That won’t be enough to defend all of his points, but it will keep him on the fringes of the top 32 long enough to build his rankings at the tournaments he missed last year.  Let Milos loose on the North American hard court circuit, and it isn’t difficult to imagine him cracking the top ten.

Federer in Straight Sets

During the telecast of today’s match between Roger Federer and Andreas Seppi, commentator Jason Goodall mentioned an interesting stat.  Federer has won more of his matches in straight sets since losing the number one ranking than he did while ranked number one.

Just about every stat from Roger’s reign at number one is impressive.  Not counting Davis Cup or matches that ended in retirement, Federer played 432 matches while atop the rankings, and won 383 (88.7%) of them.  He won 284 of those matches in straight sets.  That’s 65.7% of all matches, and 74.2% of his wins.

Since losing the top spot, Roger has played 189 matches, and won 162 (85.7%) of them.  (Still pretty good, eh?)  In that time span, he has won 125 matches in straight sets–66.1% of all matches, and 77.2% of his wins.

Both numbers are better, though not much.  The story here isn’t that he is suddenly more dominant in his wins–the increases aren’t enough for that.  Instead, the surprise is that he doesn’t seem any less dominant.  A bit of that is because some 3-set victories have turned into losses, but his modest drop in winning percentage reminds us that he still isn’t losing very many matches.  Today’s hiccup against Andreas Seppi notwithstanding, second-tier players still aren’t making many inroads against Federer.

Prospect Rankings: January 2012

With the 2012 season underway, it’s time for a look at the highest-ranked youngsters. In the “Under 23” category, you’ll see mostly familiar names. I like this list because it’s a useful reminder of who is still on the way up–Donald Young was disappointing for so long that we forget he could still mount an impressive career. To a lesser extent, the same can be said about Benoit Paire.

After Bernard Tomic’s and Benjamin Mitchell’s birthdays in the last few months, the “Under 19” cupboard is bare. It will be interesting to see who emerges from that group.

UNDER 23
25   Kei Nishikori         JPN  12/29/89  
31   Milos Raonic          CAN  12/27/90  
39   Donald Young          USA   7/23/89  
42   Bernard Tomic         AUS  10/21/92  
76   Grigor Dimitrov       BUL   5/16/91  
79   Ryan Harrison         USA    5/7/92  
81   Cedrik-Marcel Stebe   GER   10/9/90  
95   Benoit Paire          FRA    5/8/89  
117  Martin Klizan         SVK   7/11/89  
119  Vasek Pospisil        CAN   6/23/90  
125  Richard Berankis      LTU   6/21/90  
133  Thomas Schoorel       NED    4/8/89  
135  Alessandro Giannessi  ITA   5/30/90  
136  Pablo Carreno         ESP   7/12/91  
144  Evgeny Donskoy        RUS    5/9/90  
157  Facundo Bagnis        ARG   2/27/90  
163  Maxime Teixeira       FRA   1/18/89  
165  Aljaz Bedene          SLO   7/18/89  
166  Federico del Bonis    ARG   10/5/90  
172  Gastao Elias          POR  11/24/90  
UNDER 21
42   Bernard Tomic      AUS  10/21/92  
76   Grigor Dimitrov    BUL   5/16/91  
79   Ryan Harrison      USA    5/7/92  
136  Pablo Carreno      ESP   7/12/91  
173  Tsung-Hua Yang     TPE   3/20/91  
184  Javier Marti       ESP   1/11/92  
198  Laurynas Grigelis  LTU   8/14/91  
222  Andrey Kuznetsov   RUS   2/22/91  
227  Benjamin Mitchell  AUS  11/30/92  
275  James Duckworth    AUS   1/21/92  
276  Denis Kudla        USA   8/17/92  
277  Facundo Arguello   ARG    8/4/92  
287  Guilherme Clezar   BRA  12/31/92  
290  Mirza Basic        BIH   7/12/91  
293  Julien Obry        FRA    9/4/91  
321  Nicolas Pastor     ARG   3/12/91  
328  Agustin Velotti    ARG   5/24/92  
331  Kevin Krawietz     GER   1/24/92  
339  Damir Dzumhur      BIH   5/20/92  
345  Yuki Bhambri       IND    7/4/92
UNDER 19
453  Roberto Carballes-Baena      ESP    3/23/93  
463  Tiago Fernandes              BRA    1/29/93  
466  Taro Daniel                  JPN    1/27/93  
486  Andres Artunedo-Martinavarr  ESP    9/14/93  
530  Jason Kubler                 AUS    5/19/93  
569  Bruno Sant'Anna              BRA    7/12/93  
578  Edoardo Eremin               ITA    10/5/93  
603  Jiri Vesely                  CZE    7/10/93  
612  Joao Pedro Sorgi             BRA   10/18/93  
638  Dominic Thiem                AUT     9/3/93  
643  Oliver Golding               GBR    9/29/93  
662  Liam Broady                  GBR     1/4/94  
680  Juan Ignacio Londero         ARG    8/15/93  
715  Hong Chung                   KOR    5/16/93  
726  George Morgan                GBR     2/7/93  
728  Sebastien Boltz              FRA     4/5/93  
739  Denis Yevseyev               KAZ    5/22/93  
785  Bjorn Fratangelo             USA    7/19/93  
788  Matias Sborowitz             CHI     7/9/93

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.

What Happens When You Win an Aussie Warmup?

Italian translation at settesei.it

Because of its placement on the calendar, the Australian Open is unique.  It almost immediately follows the offseason (such as it is), so the common perception is that some players show up less ready than for the other three slams.

For this reason, the tournaments in the two weeks before the Australian Open are both important and difficult to predict.  At Chennai next week, who will be in shape? Who is mentally ready for the new season?  And once we get the results from Chennai, Doha, Auckland, Sydney, and Brisbane, what does that tell us about the Aussie Open itself?

It’s this last question that I’ll try to answer today.  If there’s ever a time that rankings don’t seem to count for quite as much, it’s January–after all, that’s when Yevgeny Kafelnikov won his hard-court slam.  It would stand to reason if the warmups were particularly predictive.  Perhaps tourneys like Doha serve as sneak previews of each player’s readiness for the big event in Melbourne.

Alas, it doesn’t look that way.  Winning a tournament in the two weeks before Melbourne doesn’t predict better performance at the Australian Open.  In fact, it more reliably forecasts a disappointing showing at the first grand slam of the year.

Since 1992 (and not counting 2007, when some of the warmups tinkered with a round-robin format), there have been 93 tournaments in the two weeks before Melbourne.  42 of those were the week before the slam, and 51 were two weeks before the slam.  For each one, I noted the winner of the event, their seeding in Melbourne, and their performance in Melbourne.  With the last two data points, we can determine whether each player performed equal to, above, or below expectations.

(Aussie Open seeding isn’t a perfect way to determine expectations, since results from two weeks before are reflected in the rankings.   But it was much easier than any alternative, and since this approach doesn’t recognize a difference between, say, the 5th seed and the 8th seed, I doubt it makes much difference.)

Let’s start with winners the week before Melbourne.  I didn’t expect much here, since the best players tend to take a week off before slams.  It seems, though, that a win the week before at least helps you through the first round or two.

Of the 42 champions of week-before tourneys, 12 met expectations (that is, played as their Aussie Open seeding would have predicted), 17 exceeded expectations, and 13 didn’t meet expectations (including one who withdrew from the slam).  Of the last group, only four players lost their opening round in Melbourne, and none of those players were seeded.  Several week-before winners lost in the second round; the most painful of those was 6th-seed Michael Chang’s exit in 1993.

On the flip side, Pete Sampras played Sydney and won in 1994, then went straight to Melbourne, where he made it two trophies in a row.  He is the only player in the last 20 years to have won the Australian in addition to an event the previous week.

For champions two weeks before Melbourne, the results aren’t as pleasant.  Of those 51 tournament winners, 15 met expectations at the slam, 12 exceeded them, and 24 failed to play up to their seed (again, including one who withdrew from the Open).

A whopping 14 of those 51 champions didn’t win a single match in Melbourne, including 4-seed Boris Becker in 1993, 5-seed Carlos Moya in 2005, and 9-seed Andy Murray in 2008.  Only two of the 51 players won the tournament: Petr Korda in 1998 and Roger Federer in 2006, both of whom won Doha in their respective years.

In other words, winning a warmup doesn’t say much about your form for the Open itself–in fact, next week’s winners won’t deserve much additional hype, no matter how good they look in their season debuts.

The question I haven’t answered is: What if you skip warmups altogether?  With the exception of exhibitions, that’s what Novak Djokovic is doing this year, along with several others.  Most notable from the list: Marin Cilic, who won in Chennai two years ago.  After that performance, he failed to get past the round of 16 in Melbourne.  Maybe this year, fresher legs will translate into a deeper run.

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.

Bernoulli and Court Tennis

Italian translation at settesei.it

As if you needed more proof that there’s nothing new under the sun.

Most of us are fairly new to the mathematical study of tennis.  It turns out that probabilistic analysis of tennis goes back almost as far as probability theory itself, to Jacob Bernoulli, a Swiss mathematician best known for the Law of Large Numbers.

In the late 17th century, Bernoulli wrote a Letter to a Friend on Sets in Court Tennis.  I haven’t given it a thorough reading yet, but for now, I have to share a line that ought to be the epigram to just about every work of statistical analysis in sport:

You cannot conceive, as you say, that one could measure the strengths of players with numbers, much less that one could draw from these numbers all the conclusions I have drawn.

Bernoulli was born, taught, and died in Basel, which must be why tennis is still so popular there today.