Category Archives: U.S. Open

October 26, 2012 · 11:27 am

Which Tournaments Award Competitive Wild Cards?

For the last two days, we’ve looked at tour-level wild cards from various angles.  Many top players never received any; others have gotten plenty but never taken much advantage.  Still others have managed to prop up their rankings with occasional wild cards despite not having the game to take themselves to the next level.

Wild cards are perhaps most interesting from a structural perspective.  Every tournament gets to give away between three and eight free spots in the main draw, and what they do with them is fascinating.  Events must pick from among several priorities: Bring in the best possible players to build a competitive field? Award places to big names, even if they are unlikely to win more than a single match?  Support national objectives (and perhaps invest in future fan interest) by handing the places to the best rising stars the home country has to offer?

Obviously, these priorities conflict.  The Canada Masters events give out most of their wild cards to Canadians–56 of the last 59.  But those local favorites have failed to win even one quarter of their matches, the second worst record for home-country wild cards among the current Masters events.  Wimbledon is the least home-friendly of the Grand Slams, but perhaps it is still too friendly, as British wild cards have won barely one in five matches over the last 15 years.  Lately, it has been even worse.

The dilemma is most pronounced for tournaments in countries without a strong tennis presence.  These events generally hand out most of their wild cards to non-locals, saving a few for the best the homeland has to offer.  Dubai, for instance, has only awarded 10 of its last 42 wild cards to Emiratis.  Unfortunately, those guys have gone 0-10.  The story is similar in Doha and Kuala Lumpur.

A different approach is evident in Tokyo, the only remaining tournament in Japan.  These days, the 32-player draw only gives the event three wild cards to work with.  The tournament isn’t wasting spots on outsiders: Every wild card since 1992 has gone to a Japanese player.  The local wild cards have done better than we might guess, winning almost 30% of their matches, good for 45th among the 65 tournaments I looked at.

In fact, there is not a strong correlation between home-country favoritism and poor wild-card performance.  Of long-running tournaments, Newport has seen their wild cards have the most success, winning more than half their matches.  Next on the list is Halle, also a bit better than half.  But the two tournaments take drastically different approaches to local players.  Newport only awards 63% of its WCs to Americans–second-lowest among tourneys in the USA.  Halle, on the other hand, gives nearly all of its free spots to Germans.

When discussing the structural biases of the wild card system, it’s easy to pick on the USA.  America hosts far more tournaments than any other country, and thus US events have the most wild cards at their discretion.  Many of those decisions are made by a single organization, the USTA.  But US tournaments are far from consistent in their approach.

The US Open is by far the most nationalistic of the Grand Slams, having awarded about 85% of its WCs in the last 15 years to US players.  The French comes next at 78%, then the Australian at 69%, followed by Wimbledon at 67%.  But even that understates the case.  Take out the French reciprocal wild cards since 2008 and the Australian reciprocals since 2005, and 100 of the last 105 wild cards in Flushing have represented the home nation.

Yet as we’ve seen, Newport shows less home-country favoritism than almost any other ATP event, and the Miami Masters is even more extreme, living up to its billing as the “South American Slam” by giving barely half of its wild cards to US players.  Even the most biased US tournament (aside from the Open) is the clay court event in Houston, which isn’t even in the top third of all events, handing out “only” 86% of wild cards to Americans.

The problem isn’t the behavior of US tournament officials–if anything, they are more international in their thinking than their colleagues in other countries.  Instead, their priorities–put home-country players on the court; amass a competitive field–combined with the sheer number of US events, result in one wild card after another for a small group of Americans and no equivalent advantages for players from countries that do not host tour-level events.

After the jump, find a table with many of the numbers I’ve referred to throughout this post.  All tour-level events that took place in 2011 or 2012 are included, and data goes back to 1998. homeWC% is percentage of WCs that went to home- country players, WCW% is the winning percentage of all wild cards, and hWCW% is win% of all wild cards from the home country.  I’ve excluded wild cards who were seeded, since those are usually just late entries, and don’t reflect tournament priorities in the same way that other WCs do.  For a sortable table with even more data, click here.

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Filed under U.S. Open, Wild cards

September 3, 2012 · 3:00 pm

Withdrawal Effects

Yesterday, Mardy Fish withdrew from his fourth-round match against Roger Federer.  As we saw earlier today, Federer may gain some benefit from the extra rest, but with the additional rest days built into the grand slam schedule, Roger runs the risk of getting too little time on court.

What’s the true effect, then?  Will the extra rest make Federer an even bigger favorite in his quarterfinal match against Tomas Berdych?  Or will match-court rust hold him back?

As it turns out, there is virtually no effect.  Players handed a walkover win almost exactly half of their next matches, and a closer look at those matches reveals that 50% is about what we would’ve expected from them, walkover or not.

To hunt for a potential relationship, I found 139 ATP main draw walkovers since 2001 where the winner went on to play another match at the same tournament–in other words, excluding finals.  While it may seem that players tend to withdraw when they’re least likely to win a match (as with Fish this week, or like the other two players to withdraw before facing Federer this year), there’s nothing to that theory, either. The average pre-match odds of the withdrawing player are about 51%.

Thus, we can work on the assumption that there’s little bias in the pool of 139 men who received a free pass to the next round.  For every Federer, there’s a Donald Young advancing uncontested over Richard Gasquet.  Balancing the withdrawals of players without a chance may be higher-ranked players who are quicker to withdraw because their success allows them to play it safe and make longer-term decisions.

In the 139 follow-up matches, our players went 67-72, winning 48.2% of the time.  Prematch predictions (generated by Jrank) would have projected a winning percentage of 48.9%.

If we narrow the search to slams, we get a nearly-meaningless pool of only 12 matches.  The player coming off the walkover went 6-6; prematch numbers would’ve predicted 7-5.  Perhaps rust does play a small part; considerably more likely is that the walkover simply doesn’t affect the beneficiary.

For Federer fans, though, there’s little reason for concern.  This is the ninth time in his career he’s advanced via walkover, and he’s only lost the next match twice.  One of those was in 2002.  The other was in Indian Wells in 2008.  The man who beat Fed?  Mardy Fish.

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Filed under Roger Federer, U.S. Open, Withdrawals and Retirements

· 11:45 am

At Slams, Do Shorter Matches Lead to Later Success?

Over the weekend, Tom Perrotta made the claim that grand slam champions such as Roger Federer and Serena Williams got that way, in part, by keeping early matches short.  In his words: “They’re great at not being exhausted.”

This is intuitively appealing, especially after a third round in which Federer and Novak Djokovic barely broke a sweat, while Andy Murray, David Ferrer, and Tomas Berdych each dropped a set.  (Even Juan Martin Del Potro was forced to a tiebreak by Leonardo Mayer.)

Before we get carried away, let’s find out what the numbers tell us.  As we’ll see, slam champions usually are the men who spent fewer minutes on court getting to the final.  It’s less clear, though, whether there is a causal link: After all, a better player should have an easier time of it in the early going.

The ATP has complete match-length numbers for our purposes going back to 2001.  That gives us enough data to look at the last 47 slams.

In the last 47 grand slam finals, the favorite (defined simply as the guy with the better ATP ranking) won 33 times.  In 6 of the 14 slam finals in which the underdog won, the underdog had spent less time on court in his previous six matches than the favorite did in his.  Pretty good, huh?

One problem: Six other times, the favorite won the final despite having spent more time on court.  So if you have to pick between the favorite and the better-rested player, there’s nothing in this sample to differentiate your choices.

A more positive takeaway occurs when the favorite has spent less time on court.  There have been 35 such finals since 2001, and the better-rested favorite has gone 27-8.  Most of the time, the favorite has reached the final expending less effort than his challenger did, and perhaps we can view that as a confirmation of his status as favorite.

(If you prefer games played to minutes on court, perhaps in deference to the Nadal and Djokovic speed of play, rest assured the numbers come out almost identical.  There are a few cases where players spent less time on court but played more games–or vice versa–but if the analysis above replaced minutes with games, the results would be the same.)

All else equal, we’d bet on the finalist who has spent less time on court.  But that doesn’t necessary imply that the better-rested player is more likely to win the final because he hasn’t spent as much time on court.  That seems particularly true at slams, where players almost always get a day of rest between matches, and where top contenders almost never play doubles.

More likely is that one player spent less time on court because he is the favorite.  Surely no one was surprised when Federer breezed past Verdasco, and few were surprised that Murray needed more time to put away Feliciano Lopez.  Time on court is a clue that one man is playing better tennis, regardless of whether the extra rest aids him in later matches.

We can probably all agree on a safer claim: All else equal, the world’s best would certainly prefer to spend less time on court, even if it doesn’t boost his odds of winning the final.  It might be gratifying to fight off an early challenge, but surely it’s more enjoyable to remind the rest of the field why you’re the favorite.

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Filed under Match length, U.S. Open

August 27, 2012 · 11:28 am

2012 US Open Men’s Projections

Here are my pre-tournament odds for the 2012 US Open.  For some background reading, follow the links for more on my player rating system, current rankings, and more on how I simulate tournaments.

I’ve made one tweak to the algorithm (for men only) since last posting odds.  As many of you have noticed, I seem to underestimate the chances that the very best players will progress through the draw.  Some analysis of past results showed that this is correct, so for now, there’s a bit of a band-aid in the system, boosting the odds of the current top ten in a way that reflects how they’ve outperformed my projections in the past.

Still, Federer and Djokovic both have well under 30% chances of winning the Open, and fall just short of 50% between them.  My rankings give Djokovic a very slight edge despite Federer’s big season, and the tournament draw, which places Murray in Federer’s half, firmly tilts the scales in the Serb’s favor.

    Player                    R64    R32    R16        W  
1   Roger Federer           90.6%  84.0%  74.0%    23.2%  
    Donald Young             9.4%   5.4%   2.5%     0.0%  
    Maxime Authom           32.9%   2.3%   0.7%     0.0%  
    Bjorn Phau              67.1%   8.3%   3.7%     0.0%  
    Albert Ramos            50.1%  15.1%   1.7%     0.0%  
    Robby Ginepri           49.9%  14.8%   1.7%     0.0%  
    Rui Machado             15.1%   5.5%   0.4%     0.0%  
25  Fernando Verdasco       84.9%  64.6%  15.4%     0.3%  

    Player                    R64    R32    R16        W  
23  Mardy Fish              77.1%  50.6%  33.9%     1.3%  
    Go Soeda                22.9%   8.8%   3.3%     0.0%  
    Nikolay Davydenko       88.6%  39.4%  21.4%     0.2%  
    Guido Pella             11.4%   1.2%   0.1%     0.0%  
    Ivo Karlovic            67.5%  34.2%  14.7%     0.1%  
    Jimmy Wang              32.5%  10.9%   3.0%     0.0%  
    Michael Russell         35.7%  16.2%   5.4%     0.0%  
16  Gilles Simon            64.3%  38.6%  18.1%     0.3%  

    Player                    R64    R32    R16        W  
11  Nicolas Almagro         52.9%  33.6%  20.2%     0.3%  
    Radek Stepanek          47.1%  28.5%  16.5%     0.2%  
    Nicolas Mahut           48.7%  18.2%   8.6%     0.0%  
    Philipp Petzschner      51.3%  19.6%   9.5%     0.0%  
    Blaz Kavcic             45.9%  15.3%   4.8%     0.0%  
    Flavio Cipolla          54.1%  19.8%   6.9%     0.0%  
    Jack Sock               19.8%   7.7%   1.9%     0.0%  
22  Florian Mayer           80.2%  57.2%  31.6%     0.5%  

    Player                    R64    R32    R16        W  
27  Sam Querrey             64.9%  51.7%  27.6%     0.7%  
    Yen-Hsun Lu             35.1%  23.9%   9.3%     0.1%  
    Ruben Ramirez Hidalgo   31.4%   4.8%   0.8%     0.0%  
    Somdev Devvarman        68.6%  19.6%   5.5%     0.0%  
    Denis Istomin           62.4%  23.8%  11.8%     0.1%  
    Jurgen Zopp             37.6%  10.2%   3.8%     0.0%  
    David Goffin            28.7%  14.8%   6.9%     0.0%  
6   Tomas Berdych           71.3%  51.3%  34.3%     1.7%  

    Player                    R64    R32    R16        W  
3   Andy Murray             87.6%  76.3%  63.9%    13.7%  
    Alex Bogomolov Jr.      12.4%   6.3%   2.7%     0.0%  
    Hiroki Moriya           22.9%   1.8%   0.4%     0.0%  
    Ivan Dodig              77.1%  15.7%   7.8%     0.1%  
    Thomaz Bellucci         65.9%  29.0%   6.6%     0.1%  
    Pablo Andujar           34.1%   9.9%   1.4%     0.0%  
    Robin Haase             31.9%  15.6%   3.0%     0.0%  
30  Feliciano Lopez         68.1%  45.5%  14.1%     0.3%  

    Player                    R64    R32    R16        W  
24  Marcel Granollers       63.8%  37.7%  19.2%     0.2%  
    Denis Kudla             36.2%  16.4%   6.3%     0.0%  
    Lukas Lacko             46.7%  20.6%   8.4%     0.0%  
    James Blake             53.3%  25.2%  10.8%     0.1%  
    Paul-Henri Mathieu      45.6%  14.3%   5.9%     0.0%  
    Igor Andreev            54.4%  19.2%   8.7%     0.0%  
    Santiago Giraldo        30.9%  16.5%   7.7%     0.0%  
15  Milos Raonic            69.1%  50.0%  33.0%     1.0%  

    Player                    R64    R32    R16        W  
12  Marin Cilic             70.6%  56.4%  31.1%     0.9%  
    Marinko Matosevic       29.4%  18.6%   6.5%     0.0%  
    Daniel Brands           70.6%  20.5%   6.0%     0.0%  
    Adrian Ungur            29.4%   4.5%   0.7%     0.0%  
    Tim Smyczek             53.1%  15.1%   5.8%     0.0%  
    Bobby Reynolds          46.9%  12.1%   4.3%     0.0%  
    Guido Andreozzi          5.7%   0.9%   0.1%     0.0%  
17  Kei Nishikori           94.3%  71.9%  45.6%     1.7%  

    Player                    R64    R32    R16        W  
32  Jeremy Chardy           84.1%  55.5%  23.6%     0.3%  
    Filippo Volandri        15.9%   4.3%   0.7%     0.0%  
    Tatsuma Ito             44.6%  16.6%   4.5%     0.0%  
    Matthew Ebden           55.4%  23.6%   7.3%     0.0%  
    Martin Klizan           42.3%   8.7%   3.2%     0.0%  
    Alejandro Falla         57.7%  14.7%   6.4%     0.0%  
    Karol Beck              16.7%   8.2%   3.2%     0.0%  
5   Jo-Wilfried Tsonga      83.3%  68.5%  51.2%     3.9%  

    Player                    R64    R32    R16        W  
8   Janko Tipsarevic        81.6%  69.4%  49.7%     1.9%  
    Guillaume Rufin         18.4%  10.4%   3.8%     0.0%  
    Brian Baker             40.9%   7.1%   1.8%     0.0%  
    Jan Hajek               59.1%  13.1%   4.5%     0.0%  
    Grega Zemlja            55.9%  22.5%   8.1%     0.0%  
    Ricardo Mello           44.1%  15.5%   4.7%     0.0%  
    Cedrik-Marcel Stebe     39.2%  21.6%   8.2%     0.0%  
29  Viktor Troicki          60.8%  40.4%  19.2%     0.2%  

    Player                    R64    R32    R16        W  
19  Philipp Kohlschreiber   54.1%  32.9%  16.2%     0.3%  
    Michael Llodra          45.9%  26.1%  11.9%     0.2%  
    Grigor Dimitrov         54.9%  23.7%   9.8%     0.1%  
    Benoit Paire            45.1%  17.4%   6.4%     0.0%  
    Mikhail Kukushkin       46.2%  14.5%   6.0%     0.0%  
    Jarkko Nieminen         53.8%  18.3%   8.2%     0.1%  
    Xavier Malisse          33.7%  19.2%   9.6%     0.1%  
9   John Isner              66.3%  48.0%  31.9%     1.6%  

    Player                    R64    R32    R16        W  
13  Richard Gasquet         82.1%  51.9%  27.6%     0.9%  
    Albert Montanes         17.9%   5.3%   1.3%     0.0%  
    Jurgen Melzer           82.7%  39.6%  18.1%     0.3%  
    Bradley Klahn           17.3%   3.1%   0.5%     0.0%  
    Steve Johnson           35.5%   5.3%   1.1%     0.0%  
    Rajeev Ram              64.5%  15.4%   4.7%     0.0%  
    Ernests Gulbis          27.6%  18.4%   7.6%     0.0%  
21  Tommy Haas              72.4%  60.9%  39.1%     2.5%  

    Player                    R64    R32    R16        W  
28  Mikhail Youzhny         68.2%  49.4%  22.9%     0.6%  
    Gilles Muller           31.8%  17.4%   5.2%     0.0%  
    Tobias Kamke            48.9%  15.9%   4.2%     0.0%  
    Lleyton Hewitt          51.1%  17.2%   4.6%     0.0%  
    Igor Sijsling           69.4%  17.1%   7.3%     0.0%  
    Daniel Gimeno-Traver    30.6%   4.0%   1.0%     0.0%  
    Kevin Anderson          27.6%  18.3%   9.8%     0.1%  
4   David Ferrer            72.4%  60.6%  44.9%     3.9%  

    Player                    R64    R32    R16        W  
7   Juan Martin Del Potro   70.1%  55.3%  45.2%     4.6%  
    David Nalbandian        29.9%  18.4%  12.2%     0.3%  
    Benjamin Becker         48.9%  12.7%   7.0%     0.0%  
    Ryan Harrison           51.1%  13.6%   7.7%     0.1%  
    Lukasz Kubot            71.1%  38.8%  11.8%     0.1%  
    Leonardo Mayer          28.9%  10.0%   1.5%     0.0%  
    Tommy Robredo           31.0%  11.8%   2.1%     0.0%  
26  Andreas Seppi           69.0%  39.5%  12.5%     0.1%  

    Player                    R64    R32    R16        W  
20  Andy Roddick            89.4%  57.3%  36.9%     1.1%  
    Rhyne Williams          10.6%   2.0%   0.4%     0.0%  
    Carlos Berlocq          23.0%   5.2%   1.5%     0.0%  
    Bernard Tomic           77.0%  35.5%  19.7%     0.3%  
    Edouard Roger-Vasselin  44.4%  14.4%   4.3%     0.0%  
    Fabio Fognini           55.6%  21.1%   7.3%     0.0%  
    Guillermo Garcia-Lopez  38.8%  22.5%   8.9%     0.0%  
10  Juan Monaco             61.2%  41.9%  21.0%     0.4%  

    Player                    R64    R32    R16        W  
14  Alexandr Dolgopolov     61.8%  36.8%  19.6%     0.3%  
    Jesse Levine            38.2%  18.1%   7.7%     0.0%  
    Marcos Baghdatis        67.8%  34.5%  17.2%     0.2%  
    Matthias Bachinger      32.2%  10.6%   3.5%     0.0%  
    Steve Darcis            59.5%  23.6%  10.8%     0.1%  
    Malek Jaziri            40.5%  12.6%   4.6%     0.0%  
    Sergiy Stakhovsky       28.8%  14.1%   5.8%     0.0%  
18  Stanislas Wawrinka      71.2%  49.8%  30.9%     0.8%  

    Player                    R64    R32    R16        W  
31  Julien Benneteau        64.7%  43.7%   9.6%     0.3%  
    Olivier Rochus          35.3%  18.7%   2.8%     0.0%  
    Dennis Novikov          34.1%   9.6%   1.0%     0.0%  
    Jerzy Janowicz          65.9%  28.1%   4.4%     0.0%  
    Rogerio Dutra Silva     39.5%   2.5%   0.6%     0.0%  
    Teymuraz Gabashvili     60.5%   5.4%   1.9%     0.0%  
    Paolo Lorenzi            6.4%   3.6%   1.2%     0.0%  
2   Novak Djokovic          93.6%  88.6%  78.5%    26.5%

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Filed under Forecasting, U.S. Open

· 11:12 am

2012 US Open Women’s Projections

Here are my pre-tournament odds for the 2012 US Open.  For some background reading, follow the links for more on my player rating system, current rankings, and more on how I simulate tournaments.

    Player                         R64    R32    R16        W  
1   Victoria Azarenka            92.6%  83.5%  70.0%    12.5%  
    Alexandra Panova              7.4%   3.2%   1.0%     0.0%  
    Barbora Zahlavova Strycova   46.8%   6.0%   2.1%     0.0%  
    Kirsten Flipkens             53.2%   7.3%   2.7%     0.0%  
    Su-Wei Hsieh                 56.4%  24.1%   5.4%     0.0%  
    Magdalena Rybarikova         43.6%  16.0%   2.9%     0.0%  
    Virginie Razzano             41.4%  22.8%   5.2%     0.0%  
28  Jie Zheng                    58.6%  37.1%  10.6%     0.2%  

    Player                         R64    R32    R16        W  
18  Julia Goerges                80.7%  66.0%  37.5%     0.8%  
    Kristyna Pliskova            19.3%  10.1%   2.6%     0.0%  
    Mandy Minella                50.2%  12.0%   3.0%     0.0%  
    Olivia Rogowska              49.8%  11.9%   2.9%     0.0%  
    Stephanie Foretz Gacon       43.0%   7.4%   2.0%     0.0%  
    Anna Tatishvili              57.0%  12.4%   4.0%     0.0%  
    Sorana Cirstea               40.3%  30.8%  16.9%     0.2%  
16  Sabine Lisicki               59.7%  49.4%  31.2%     0.8%  

    Player                         R64    R32    R16        W  
9   Na Li                        85.7%  75.7%  41.9%     4.6%  
    Heather Watson               14.3%   8.0%   1.6%     0.0%  
    Lesia Tsurenko               45.0%   6.6%   1.0%     0.0%  
    Casey Dellacqua              55.0%   9.7%   1.8%     0.0%  
    Samantha Crawford            14.0%   0.5%   0.0%     0.0%  
    Laura Robson                 86.0%  14.2%   3.6%     0.0%  
    Victoria Duval                0.9%   0.1%   0.0%     0.0%  
23  Kim Clijsters                99.1%  85.3%  50.1%     5.5%  

    Player                         R64    R32    R16        W  
31  Varvara Lepchenko            66.9%  44.1%  15.7%     0.0%  
    Mathilde Johansson           33.1%  16.1%   3.7%     0.0%  
    Anastasia Rodionova          55.9%  23.4%   5.9%     0.0%  
    Julia Cohen                  44.1%  16.4%   3.5%     0.0%  
    Edina Gallovits-Hall         44.2%   7.1%   2.7%     0.0%  
    Stefanie Voegele             55.8%  10.8%   4.7%     0.0%  
    Petra Martic                 25.5%  17.6%  10.7%     0.0%  
7   Samantha Stosur              74.5%  64.5%  53.0%     2.1%  

    Player                         R64    R32    R16        W  
3   Maria Sharapova              86.5%  77.7%  67.0%     9.3%  
    Melinda Czink                13.5%   7.9%   4.1%     0.0%  
    Lourdes Dominguez Lino       48.9%   6.9%   3.0%     0.0%  
    Sesil Karatantcheva          51.1%   7.4%   3.3%     0.0%  
    Timea Bacsinszky             70.8%  19.4%   2.5%     0.0%  
    Mallory Burdette             29.2%   3.9%   0.3%     0.0%  
    Lucie Hradecka               38.0%  27.1%   5.7%     0.0%  
27  Anabel Medina Garrigues      62.0%  49.6%  14.1%     0.1%  

    Player                         R64    R32    R16        W  
19  Nadia Petrova                67.0%  36.1%  19.5%     0.2%  
    Jarmila Gajdosova            33.0%  12.0%   4.4%     0.0%  
    Simona Halep                 49.9%  25.8%  12.8%     0.1%  
    Iveta Benesova               50.1%  26.1%  13.0%     0.1%  
    Alexandra Cadantu            21.3%   4.5%   1.0%     0.0%  
    Aleksandra Wozniak           78.7%  37.7%  18.7%     0.2%  
    Melanie Oudin                30.9%  13.9%   5.3%     0.0%  
15  Lucie Safarova               69.1%  43.9%  25.4%     0.4%  

    Player                         R64    R32    R16        W  
11  Marion Bartoli               78.4%  46.4%  28.9%     1.2%  
    Jamie Hampton                21.6%   6.4%   2.1%     0.0%  
    Romina Oprandi               24.5%   7.1%   2.3%     0.0%  
    Andrea Petkovic              75.5%  40.2%  23.9%     0.7%  
    Kristina Mladenovic          37.5%   7.2%   1.4%     0.0%  
    Marina Erakovic              62.5%  17.6%   4.9%     0.0%  
    Daniela Hantuchova           48.8%  36.5%  17.6%     0.4%  
17  Anastasia Pavlyuchenkova     51.2%  38.7%  18.9%     0.5%  

    Player                         R64    R32    R16        W  
25  Yanina Wickmayer             82.8%  64.6%  26.3%     0.6%  
    Julia Glushko                17.2%   7.3%   1.2%     0.0%  
    Pauline Parmentier           45.4%  11.9%   2.1%     0.0%  
    Michaella Krajicek           54.6%  16.2%   3.3%     0.0%  
    Nicole Gibbs                 23.5%   1.7%   0.3%     0.0%  
    Alize Cornet                 76.5%  15.0%   5.8%     0.0%  
    Polona Hercog                15.7%   9.2%   3.9%     0.0%  
5   Petra Kvitova                84.3%  74.0%  57.1%     6.9%  

    Player                         R64    R32    R16        W  
8   Caroline Wozniacki           85.1%  72.5%  52.5%     4.1%  
    Irina-Camelia Begu           14.9%   7.6%   2.4%     0.0%  
    Silvia Soler-Espinosa        57.0%  12.3%   4.3%     0.0%  
    Alla Kudryavtseva            43.0%   7.6%   2.2%     0.0%  
    Tsvetana Pironkova           68.7%  48.3%  22.8%     0.5%  
    Camila Giorgi                31.3%  16.4%   5.2%     0.0%  
    Ayumi Morita                 37.6%  10.7%   2.6%     0.0%  
26  Monica Niculescu             62.4%  24.5%   8.0%     0.0%  

    Player                         R64    R32    R16        W  
22  Francesca Schiavone          55.4%  41.9%  18.9%     0.2%  
    Sloane Stephens              44.6%  31.9%  12.9%     0.1%  
    Akgul Amanmuradova           52.9%  14.2%   3.3%     0.0%  
    Tatjana Malek                47.1%  12.0%   2.5%     0.0%  
    Kimiko Date-Krumm            29.2%   5.8%   1.8%     0.0%  
    Sofia Arvidsson              70.8%  25.3%  13.4%     0.1%  
    Elina Svitolina              13.8%   4.5%   1.4%     0.0%  
12  Ana Ivanovic                 86.2%  64.4%  45.8%     1.6%  

    Player                         R64    R32    R16        W  
14  Maria Kirilenko              67.6%  50.9%  31.6%     0.6%  
    Chanelle Scheepers           32.4%  19.5%   8.6%     0.0%  
    Agnes Szavay                 16.2%   1.4%   0.2%     0.0%  
    Greta Arn                    83.8%  28.2%  11.2%     0.0%  
    Galina Voskoboeva            59.1%  30.2%  15.0%     0.1%  
    Arantxa Rus                  40.9%  17.3%   7.1%     0.0%  
    Andrea Hlavackova            30.0%  11.4%   4.0%     0.0%  
24  Klara Zakopalova             70.0%  41.1%  22.3%     0.2%  

    Player                         R64    R32    R16        W  
32  Shuai Peng                   57.6%  25.3%   5.2%     0.1%  
    Elena Vesnina                42.4%  15.8%   2.6%     0.0%  
    Ekaterina Makarova           80.0%  52.4%  14.9%     0.8%  
    Eleni Daniilidou             20.0%   6.5%   0.8%     0.0%  
    Mirjana Lucic                35.6%   3.0%   0.8%     0.0%  
    Maria Jose Martinez Sanchez  64.4%   8.7%   3.4%     0.0%  
    Coco Vandeweghe               8.2%   4.1%   1.3%     0.0%  
4   Serena Williams              91.8%  84.2%  70.9%    26.1%  

    Player                         R64    R32    R16        W  
6   Angelique Kerber             88.6%  65.7%  48.5%     6.0%  
    Anne Keothavong              11.4%   3.3%   1.0%     0.0%  
    Bethanie Mattek-Sands        30.1%   6.3%   2.4%     0.0%  
    Venus Williams               69.9%  24.7%  13.9%     0.4%  
    Johanna Konta                42.7%  10.2%   1.6%     0.0%  
    Timea Babos                  57.3%  16.6%   3.3%     0.0%  
    Olga Govortsova              18.2%   8.4%   1.4%     0.0%  
29  Tamira Paszek                81.8%  64.8%  27.9%     1.1%  

    Player                         R64    R32    R16        W  
21  Christina McHale             75.7%  61.4%  41.8%     1.0%  
    Kiki Bertens                 24.3%  14.2%   6.0%     0.0%  
    Olga Puchkova                39.7%   7.9%   2.4%     0.0%  
    Irina Falconi                60.3%  16.5%   6.5%     0.0%  
    Vera Dushevina               68.3%  27.2%  10.4%     0.0%  
    Nastassja Burnett            31.7%   7.5%   1.7%     0.0%  
    Garbine Muguruza             36.1%  20.5%   7.9%     0.0%  
10  Sara Errani                  63.9%  44.9%  23.3%     0.2%  

    Player                         R64    R32    R16        W  
13  Dominika Cibulkova           73.9%  54.9%  35.7%     1.3%  
    Johanna Larsson              26.1%  13.2%   5.2%     0.0%  
    Bojana Jovanovski            44.2%  13.1%   4.8%     0.0%  
    Mona Barthel                 55.8%  18.8%   8.0%     0.0%  
    Vania King                   54.1%  25.1%  11.3%     0.1%  
    Yaroslava Shvedova           45.9%  19.7%   8.2%     0.0%  
    Urszula Radwanska            45.1%  23.7%  10.9%     0.1%  
20  Roberta Vinci                54.9%  31.5%  15.9%     0.2%  

    Player                         R64    R32    R16        W  
30  Jelena Jankovic              60.9%  40.0%  14.4%     0.2%  
    Kateryna Bondarenko          39.1%  21.7%   6.0%     0.0%  
    Lara Arruabarrena-Vecino     25.7%   5.6%   0.7%     0.0%  
    Shahar Peer                  74.3%  32.6%   9.1%     0.0%  
    Ksenia Pervak                47.1%  10.4%   4.7%     0.0%  
    Carla Suarez Navarro         52.9%  12.6%   6.1%     0.0%  
    Nina Bratchikova             11.3%   4.2%   1.4%     0.0%  
2   Agnieszka Radwanska          88.7%  72.7%  57.6%     6.7%

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Filed under Forecasting, U.S. Open, WTA

August 23, 2012 · 1:16 pm

The Slam No One Misses

By now you’ve heard: Rafael Nadal will miss the US Open.  It’s hardly a surprise, as Rafa hasn’t played a match since Wimbledon, and his knee has kept him off the tour for long periods in the past.

What is remarkable is the rarity of a top player missing the Open.  Despite its location near the end of the ATP schedule, after eight months of grueling tennis in which every player picks up his share of nagging injuries, New York gets a better turnout from top-10 players than any of the other three slams.

In fact, Nadal is only the third top-three player since 1991 to skip Flushing.  In 1999, #1-ranked Pete Sampras couldn’t play, and in 2004, it was #3-ranked Guillermo Coria who stayed home.  In the tournament’s last 21 editions, a top-ten player has missed the event only ten times.

It’s interesting to speculate as to why top players manage to show up in Flushing at a rate unmatched elsewhere.  Surely the event doesn’t have more cachet than Wimbledon.  Certainly the multiple shifts of surface throughout the spring and summer test every player’s mental and physical stamina.  Perhaps the longish break between Wimbledon and the Open allows players to take time off if they need it.  Most men play Canada and Cincinnati, but as we’ve seen this year, plenty of guys are willing to miss either one, meaning that only a serious injury keeps one out of the New York draw.

Defying conventional wisdom even further, the slam with the second-best turnout among top players is the French, not Wimbledon.  Since 1991, only 13 top-tenners have missed Roland Garros, and three of those were Boris Becker.

Wimbledon may be synonymous with the sport of tennis, but it is a distant third, with 25 top-tenners missing from the last 22 draws.  Here the no-shows are more logical: Alex Corretja three times, Marcelo Rios twice, Sergi Bruguera four times.  In the late 1990s, some guys simply didn’t consider the All-England Club a must.

Australia is a bit further back in fourth, with 29 top-tenners who didn’t play.  Melbourne does seem to have the least cachet of the four big events, but the tide may be turning.  Since 2006, only one top-ten player, Nikolay Davydenko in 2009, failed to make an appearance.

It may seem that absences from Grand Slams are random, driven by accidents such as major injuries that can happen at any time.  Any single absence surely does look that way.  There are larger forces at work, however–the value associated with certain tournaments, the demands of the schedule leading to physical breakdowns at some times and not others–that are not random.  In one more way, Rafael Nadal is proving himself a unique player, missing the most unmissable slam on the ATP calendar.

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August 15, 2012 · 11:59 am

Does Cincinnati Matter in Flushing?

After months of clay and grass tournaments, the best players on tour are finally competing on hard courts.  For many, Cincinnati is the extent of their North American hard court preparation leading up to the US Open.  No matter who wins this week, we’ll be tempted to anoint him the favorite in New York.  Should we?

Traditionally, Cincinnati features one of the strongest draws of the ATP season.  As the only tournament scheduled two weeks before the US Open, there are no alternatives for players preparing for the slam, and it still allows a week off.  This year’s draw, missing three top 10 players due to injury, is an aberration.

It’s no surprise, then, that the list of winners in Cincinnati is particularly impressive.  19 of the last 20 champions have career peak rankings of 1 or 2.  (The black sheep in the group is Thomas Enqvist, who “only” reached #4.)  Not only do the best in the world show up to play, they show up to win.

More than some warmups, Cincinnati seems to tell us who is in form.  Let’s see if tells us who is going to win the Open.

Since 1991, there have been four seasons when the same man lifted the trophy in Cincinnati and New York: Pat Rafter in 1998, Andy Roddick in 2003, and Roger Federer in 2005 and 2007.  Five more times, the Cincinnati winner reached the US Open final.  Not counting 1999, when Pete Sampras didn’t compete in Flushing, the Cincinnati champion has failed to reach the US Open round of 16 only twice in the last 21 years.

So, the Cincinnati winner has won the US Open about 20% of the time, and reached the final another 25%.  Sounds good, though not as good as we’d expect from the top seed.  On the other hand, Cincinnati winners aren’t always the top seed in New York, so we can’t expect them to perform at that level.

In fact, the Cincinnati winner has been the top seed in Flushing only six times.  On average, the Cinci champion has been seeded 4th in New York.  Compared to the performance we’d expect from a #4 seed, a 20% shot at winning the tournament, along with a nearly 1-in-2 chance of reaching the final, is extremely good.

Since 1991, #4 seeds at the US Open haven’t performed nearly so well during the final weekend as have Cincinnati champions.  Both groups have a roughly 6-in-10 chance of reaching the semis (#4 seeds: 57.1%, Cinci winners: 60%), but the #4 seeds have won only half of their semifinals, for a 28.5% chance of reaching the final, compared to the 45% of Cincinnati titlists.

The biggest difference is where it matters most: the final itself.  Cincinnati winners go on to win almost half of their US Open finals, winning 4 titles in 20 attempts, as we’ve seen.  But #4 seeds have won only 2 titles.  It’s not a huge sample, but if we expand our view to consider all four slams since 1991, the performance of #4 seeds stays about the same.

Much to my surprise, it seems that Cincinnati results do have something to say about the final rounds in Flushing.  This week’s winner isn’t exactly a lock to triumph in New York, but his performance in Ohio will tell us to expect that much more from him at the US Open.

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October 13, 2011 · 10:07 am

US Open Serve Speed by Player

It’s time for more serve-speed research notes. Most of the matches at the 2011 U.S. Open were tracked by Pointstream, and serve speed was recorded for the vast majority of those points. The Open website published some serve speed numbers, but not as conveniently as I would like.

Below, find the average first and second serve speeds for every man who played three or more Pointstream-tracked matches. Oddly enough, the top and bottom of the list are held by Americans; John Isner is where you’d expect him, while Donald Young barely kept his first-serve average in the triple digits.

I didn’t expect to see nearly so much variation in the difference between first and second serve averages. Sure, Isner and Young are the endpoints in both lists, but David Nalbandian–below average on firsts–is third of 22 on seconds. To take another angle, both Marin Cilic and Jo-Wilfried Tsonga each have more than double the difference in averages than does either Alex Bogomolov or Fernando Verdasco.

(“M” is the number of matches tracked by Pointstream for each player.)

Player                 M  1sts  1stAvg  2nds  2ndAvg  
John Isner             4   313   124.5   125   106.2  
Andy Roddick           5   249   122.1   118   100.5  
Tomas Berdych          3    85   120.3    71    95.0  
Jo-Wilfried Tsonga     5   289   119.7   206    90.6  
Marin Cilic            3   125   118.7   121    86.3  
Janko Tipsarevic       3   148   116.5    84    90.5  
Roger Federer          6   355   115.6   186    94.6  
Juan Martin Del Potro  3   180   114.5    96    88.2  
Julien Benneteau       3   177   114.0    86    89.9  
Tommy Haas             3   211   113.9   124    94.1  
Novak Djokovic         7   421   113.7   226    91.4  

Player                 M  1sts  1stAvg  2nds  2ndAvg
Andy Murray            6   338   112.6   204    85.2  
Mardy Fish             4   231   112.4   165    88.0  
David Nalbandian       3   165   112.3   125    96.1  
David Ferrer           3   128   112.2    74    88.9  
Rafael Nadal           7   435   110.5   176    84.5  
Juan Monaco            3   167   109.4    70    90.4  
Gilles Simon           3   235   108.3   179    81.6  
Fernando Verdasco      3   175   107.3    72    92.6  
Alex Bogomolov Jr.     3   264   103.1    96    89.1  
Donald Young           4   213   101.9   111    80.6

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October 12, 2011 · 11:04 am

The Effect of Serve Speed

All else equal, you want to serve harder. But how much does it really matter?

That’s a more difficult question than it sounds, and I don’t yet claim to have an answer. In the meantime, I can share the results of some data crunching.

In 2011 U.S. Open matches covered by Pointstream, there were more than 9,000 first serve points. The server won almost exactly 70% of those points. About 11% of points were aces, and another 24% were service winners.

To see the effect of serve speed, I looked at four outcomes: aces, service winners, short points (three or fewer shots), and points won. It’s no surprise that each type of results happens more on faster serves.

Below, find the full numbers for serves of various speeds. The finding that sticks out to me is the small change in service points won from the 95-99 MPH group to the 115-119 MPH group. It may be that the modest increase–put another way, the surprising success rate at 95-104 MPH–is a result of strategic wide serves, or the better ground games of the players who hit slower serves.

So as I said, there’s much more work to be done, identifying the effects of faster serves for individual players, looking at deuce/ad court differences (for righties and lefties), and the results on different serve directions.

MPH      SrvPts   Ace%  SvcW%  Short%  PtsWon%  
85-89       140   2.1%  17.9%   47.1%    55.0%  
90-94       275   0.7%  21.5%   47.6%    63.6%  
95-99       546   2.2%  18.5%   48.4%    66.1%  
100-104     885   4.2%  24.6%   51.0%    66.0%  
105-109    1400   6.4%  29.3%   56.6%    68.7%  
110-114    1524   8.7%  34.0%   57.3%    69.1%  
115-119    1487  12.2%  35.9%   60.8%    69.4%  
120-124    1553  16.1%  40.1%   65.2%    73.2%  
125-129     941  21.5%  48.1%   72.4%    76.3%  
130-134     353  29.7%  58.4%   77.3%    84.4%  
135-139      66  27.3%  65.2%   80.3%    89.4%

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August 29, 2011 · 1:51 pm

US Open Forecasts: Updating Live

My U.S. Open tournament forecasts will update every 15 to 30 minutes for the next two weeks.

Click below to see each player’s chances of reaching each round:

Enjoy!

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