Category Archives: Wimbledon

How Underdogs Could Win Wimbledon Doubles

Yesterday, wildcards Jonathan Marray and Frederick Nielsen won the Wimbledon doubles title.  Nobody saw that one coming–in recent years, men’s doubles has been dominated by a small number of specialists.  When a team outside the top 10 wins an event, it’s often thanks to a top singles player or two.  Marray and Nielsen sit comfortably outside either category.

How did they do it?  Obviously, they played great tennis, winning big point after big point against some of the best doubles teams in the world.  (They played a fifth set three times in the tournament, but the Bryan brothers could only take them to four!)  Beyond that, there are structural elements making it possible: Men’s doubles has steadily become more equal, as better equipment and training have leveled the playing field.  The event is underdog-friendly, and it is particularly so at Wimbledon.

Hold machines

In most men’s doubles matches, breaks of serve are as rare as in a John Isner fifth set.  In yesterday’s final, the server won 73% of all points.  Mathematically, that translates to a hold rate of 93%, or one break every 14 games–less than one per set.  (In fact, it was even lower than that: three breaks in 53 standard games: 1 per 17.7.)

First serve percentages are even more remarkable.  Yesterday, both teams won 80% of first serve points.  In the two semfinals, more than 80% of first serve points resulted in wins for the server, and the Bryans won 85% of their first offerings.  For comparison, consider that on grass, Roger Federer’s career first serve winning percentage is 78.6%.  You get the picture: service breaks are very hard to come by.

When there are so few service breaks, sets (and by extension, matches) can hinge on a very small number of points.  Marray/Nielsen played 27 sets in the tournament, and 13 were decided in a tiebreak.  Of those 13, 11 were 7-4 or closer.  The wildcard champions squeaked through five of their six matches.

A few good points

Men’s doubles is dominated by the serve, and when the surface favors servers even more, matches–even best-of-five matches–hinge on just a few important points.  Consider Marray/Nielsen’s third-round upset of Qureshi/Rojer: 7-6(5) 7-6(4) 6-7(4) 5-7 7-5.  Essentially, 56 games–every game to the first three tiebreaks, and then to 5-5 in the final two sets–had no purpose other than wearing down the other side.  If only one or two points had gone differently in the first two sets, the AntiPak express would have won the match in the fourth set, and the Bryans would probably be lifting the trophy as usual.

This isn’t to lessen Marray/Nielsen’s achievement–far from it.  Fast-court doubles has been reduced to a thirty-point contest, and the underdog duo won all five of those mini-matches in which they found themselves.  The other 250 points function simply to prove that both teams belong there.  And any team that can win 70-75% of service points has a good chance of proving themselves.

Once you’ve reduced the match to 30 points, luck–and mental fortitude–play a bigger role.  If you’re playing Novak Djokovic on the singles court, you can be as mentally strong as you want, but if you don’t have top-ten skills, you’re going to lose.  In doubles, steely nerves at 4-4 in a breaker, maybe with a couple of lucky netcords or reflex volleys thrown in, can be enough.

While there is certainly some skill that separates the Bryan brothers from the Ratiwatana brothers, even the journeymen Thais pushed Lindstedt and Tecau to tiebreaks in two of their three first-round sets.  I hesitate to use the word “clutch,” but on Centre Court, with a hundred thousand pounds on the line, tiebreaks are about more than serves and volleys.  What the wildcards proved over the fortnight is that, at least for two weeks, they possessed the rarest of modern doubles skills: They could play the big points with the big boys.

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Filed under Doubles, Wimbledon

The Misleading Stat Sheet

A glance at the stat sheet from Serena Williams’s third-round match against Jie Zheng suggests that Serena dominated.  23 aces to 1, 3 break point conversions to none, 54 winners to 21, 84% 2nd-serve points won to 50%, and 55% of the total points played.

Of course, according to the more important stats–games and sets–Serena didn’t dominate.  She barely snuck through, losing a first-set tiebreak and going to 9-7 in the third.

Rick Devereaux, who brought this contrast to my attention, suggests that grass-court tennis–with more clean winners and fewer unforced errors than slower-paced styles–may be responsible.  That’s certainly part of the equation.

In fact, the Serena/Zheng match highlights the limits of the traditional stat sheet, especially on a surface that particularly favors the server.  Except for winners and unforced errors, nearly every stat directly captures some aspect of serving prowess–either yours or your opponent’s.  And in an era where nearly everyone is an excellent server, it doesn’t matter much whether you’ve set down a great serving performance or merely a good one.

To get to tiebreaks (or 9-7, or 70-68), you don’t have to be as good as your opponent, you just need to be good enough to hold.  Even the “winners” stat has to do with serving dominance, since so many are third shots behind a serve.  The vast majority of the stats from Serena’s match tell us that the American was more dominant on her serve than Zheng was.  And, of course, while Zheng was good enough to hold to 6-6 and 7-7, she lost the second set fairly badly, so the stats are a weighted average of two almost-even sets and one lopsided one.

When we find a mismatch between stat sheet and scoreline, we’re usually seeing one of two things:

  1. One player was much more dominant on serve (think 4 or 5-point games instead of 6+)
  2. One player won a lot of clutch points (like deuce, on serve) — losing unimportant ones (like 40-0 on serve), thus padding her opponent’s stat sheet.

Oddly, in the men’s game, the players who we think of as most dominant on serve rarely give us mismatched score sheets like this–quite the opposite.  Note the wording: “one player was much more dominant.”  There’s no doubt John Isner can dominate on serve, but since almost all his opponents are also good servers, Isner’s weak return game means that he is often the less dominant server, winning service games at 40-30 and losing return games at 0-40 or 15-40.  In fact, Isner has won more than 20 career matches despite losing more than half of the points played!

The same reasoning doesn’t apply to Serena.  She may be as big a server (relative to her opponents) as Isner, but her return game is also world-class.  And in the WTA, there are far more weak-to-middling servers.  On grass, as Rick points out, those weak-to-middling servers are (usually) still able to hold, making it more likely that a dominant performance on paper ends at 9-7 in a deciding set.

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Filed under Oddities, Wimbledon, WTA

The Greatest Upset in Sports Recency

Last night, Lukas Rosol shocked the tennis world by beating Rafael Nadal.  Immediately, the verdict was in: One of the greatest (the greatest?) upsets of all time.  Completely unthinkable.  Impossible to see coming.

And to a certain extent, that’s correct.  Nobody picked Rosol to beat Nadal; I’d be surprised if anyone went on the record forecasting that the Czech would win a single set.  But for all that, the superlatives have gone too far.  It’s one thing to predict that Djokovic/Nadal/Federer/whoever will win a certain match.  It’s another to make the broader claim that they will always beat opponents of a certain level.  The first claim is a sound one; the second is madness.

One way to look at this is a glance at the betting market.  For high-profile matches, punters and sportsbooks give us a good idea of the conventional wisdom going into a match.  Pre-match odds varied from (very roughly) 25:1 to 75:1.  Even if we go to an extreme and take odds of 100:1, that means that the market gave Rosol a 1% chance of victory.  A small chance, but far from a zero chance.

So, of course, Nadal should have gotten through to the third round–he probably should have gotten to the semifinals.  But with 1% underdogs at every step, every once in a while it’s not going to happen.  Consider that each of the top three play two matches against unseeded opponents at every slam.  That’s six opportunities at every slam for a greatest upset of all time.  The occasional first- or second-rounder doesn’t fit the bill, like Nadal-Isner at last year’s French, but later-round matches take their place, like Federer-Goffin last month.

Given 24 opportunities per year, there should be one such upset every four years.  That’s still newsworthy, but statistically speaking, it’s not the greatest upset in tennis history, it’s the greatest upset in very recent memory.  And that’s just counting slams.

No nobody

Part of the reason we overreact to these things is that our brains aren’t wired to think about small probabilities–it’s either likely or it’s not.  Another reason is the historically unprecedented dominance of the big three.

Contributing to the effect is something that Steve at Shank Tennis pointed out:  The media is inaccurately portraying Rosol as a “nobody.”  Sure, Rosol has never played a Wimbledon main draw before, and he’s beaten a top-20 opponent only once. But this is the third-ranked player from the Czech Republic, a man who has been in the top 101 for more than a year, peaking inside the top 70.  In any major team sport, a top-100 player is among the top five on his team; number 65 might make an all-star team.

When Donald Young beat Andy Murray, we were shocked, but not to the same extent–we all know about Young’s potential, and besides, American fans have been talking about him for years.  Even when Alex Bogomolov registered the same upset the following week, it was a recognizable name, also in part due to US wild cards and press attention.

Rather than dismissing yesterday’s match as a freak event involving a player who we’ll never hear from again, we’re better off to treat it as a sign of just how strong the back of the field is.  Rosol is not the only man outside of the top 50 with a thunderous game.  He’s not the only threat on tour who was never talked up as a junior.  And he’s certainly not going to be the last “journeyman” to register a high-profile upset over an “unbeatable” opponent.

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Filed under Rafael Nadal, Wimbledon

Gentlemen’s and Ladies’ Wimbledon Odds, Updating Live

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

Enjoy!

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Filed under Forecasting, Wimbledon

The Aging Wimbledon Men’s Draw

Men’s tennis is getting older, and the drift toward middle age is evident at Wimbledon this week.

Of the 128 men in the main draw, 34 are at least 30 years old, while only two are in their teens.  This is just the latest step in a trend that has been evident for at least a decade.

The 34 30-somethings are not just a modern-day record–the number blows recent years out of the water.  Last year’s main draw had 24 30-somethings, and that was the highest such total since 1979.  Teenagers have been on the wane for years–there have only been two in the main draw in each of the last four years, but as recently as 2001, there were eight.  In several years in the late 80s and early 90s, there were more teenagers than 30-year-olds.

Whatever the explanation for this–and there are many possible ones–it’s clear that something is going on.  It takes longer than it ever has for a young rising star to establish himself on tour, and top players are able to stay healthy and competitive for as long as ever before.

After the jump, find a table with more detailed results.

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Filed under Research, Wimbledon

2012 Wimbledon Men’s Projections

Here are my projections for this year’s Wimbledon men’s draw.  Djokovic is far and away the favorite now that we’ve moved away from clay.  Federer comes in a close third behind Nadal, helped in part by what is probably the easiest of the four quarters.

Intuitively, these numbers seem about right, especially for the top players.  But a few developments in the ATP recently have exposed some gaps in my ranking system.  Brian Baker’s quick ascendance has yet to do much for him in my system, in part because he hasn’t played very much top-level matches.  But after his performance in Nice, it seems wrong to give him less than a 35% chance against a journeyman like Rui Machado.

The other head-scratcher is Tommy Haas.  After winning Halle, my system isn’t giving him much credit, in large part because he’s 34. Since players start going downhill by age 26, a player’s rate of decline in his mid-30s would generally be staggering.   But, of course, most players are gone by then.  If someone like Haas is still playing (and winning), he probably isn’t subject to exactly the same laws.  Perhaps 34-year-olds on tour are rare enough that it isn’t all that important, but in this one case, it generates a forecast that doesn’t jibe with common sense.

If you’re interested in the rankings behind these forecasts, click here; for more background on the system, here.

Women’s odds were posted earlier today, and both forecasts will be updated throughout the tournament–I’ll post those links when I have them, probably mid-day Tuesday.

    Player                   R64    R32    R16        W  
1   Novak Djokovic         96.8%  81.3%  70.3%    26.2%  
    Juan Carlos Ferrero     3.2%   0.5%   0.1%     0.0%  
    Ryan Harrison          55.0%  10.7%   6.1%     0.3%  
    Yen-Hsun Lu            45.0%   7.6%   3.9%     0.1%  
    Benjamin Becker        53.2%  25.2%   4.8%     0.1%  
    James Blake            46.8%  20.5%   3.6%     0.0%  
    Sergiy Stakhovsky      54.4%  30.6%   6.7%     0.2%  
28  Radek Stepanek         45.6%  23.8%   4.7%     0.1%  

    Player                   R64    R32    R16        W  
24  Marcel Granollers      53.7%  42.9%  26.4%     1.0%  
    Viktor Troicki         46.3%  35.9%  20.9%     0.6%  
    Martin Klizan          65.0%  15.9%   5.4%     0.0%  
    Juan Ignacio Chela     35.0%   5.3%   1.1%     0.0%  
    Jeremy Chardy          88.5%  48.2%  22.9%     0.4%  
    Filippo Volandri       11.5%   1.8%   0.2%     0.0%  
    Leonardo Mayer         18.7%   4.7%   0.9%     0.0%  
15  Juan Monaco            81.3%  45.3%  22.1%     0.4%  

    Player                   R64    R32    R16        W  
12  Nicolas Almagro        60.2%  36.6%  18.6%     0.3%  
    Olivier Rochus         39.8%  20.3%   8.2%     0.0%  
    Guillaume Rufin        21.0%   4.4%   0.8%     0.0%  
    Steve Darcis           79.0%  38.8%  17.8%     0.2%  
    Carlos Berlocq         18.7%   2.6%   0.5%     0.0%  
    Ruben Bemelmans        81.3%  33.7%  16.7%     0.2%  
    Tobias Kamke           37.3%  21.0%  10.5%     0.1%  
18  Richard Gasquet        62.7%  42.7%  26.8%     1.0%  

    Player                   R64    R32    R16        W  
31  Florian Mayer          60.7%  38.2%  19.4%     0.8%  
    Dmitry Tursunov        39.3%  20.8%   8.3%     0.1%  
    Philipp Petzschner     54.8%  23.6%   9.6%     0.2%  
    Blaz Kavcic            45.2%  17.4%   6.2%     0.1%  
    Simone Bolelli         51.0%  17.4%   8.0%     0.1%  
    Jerzy Janowicz         49.0%  16.3%   7.2%     0.1%  
    Ernests Gulbis         36.3%  21.1%  11.4%     0.4%  
6   Tomas Berdych          63.7%  45.3%  29.9%     2.6%  

    Player                   R64    R32    R16        W  
3   Roger Federer          92.0%  73.7%  59.4%    10.4%  
    Albert Ramos            8.0%   2.1%   0.6%     0.0%  
    Fabio Fognini          36.9%   6.9%   3.0%     0.0%  
    Michael Llodra         63.1%  17.3%   9.6%     0.2%  
    A Menendez-Maceiras    31.5%   6.6%   0.8%     0.0%  
    Michael Russell        68.5%  25.3%   5.5%     0.0%  
    Gilles Muller          43.3%  28.2%   7.9%     0.1%  
29  Julien Benneteau       56.7%  39.9%  13.4%     0.3%  

    Player                   R64    R32    R16        W  
17  Fernando Verdasco      88.2%  53.1%  27.7%     0.8%  
    Jimmy Wang             11.8%   2.4%   0.3%     0.0%  
    Grega Zemlja           90.7%  43.6%  20.1%     0.3%  
    Josh Goodall            9.3%   0.9%   0.1%     0.0%  
    Xavier Malisse         51.6%  21.7%   9.8%     0.1%  
    Marinko Matosevic      48.4%  19.7%   8.6%     0.1%  
    Paul-Henri Mathieu     12.6%   2.5%   0.5%     0.0%  
13  Gilles Simon           87.4%  56.1%  32.8%     1.4%  

    Player                   R64    R32    R16        W  
11  John Isner             66.6%  46.0%  28.9%     1.2%  
    Alejandro Falla        33.4%  17.7%   8.2%     0.1%  
    Paolo Lorenzi          21.2%   3.4%   0.7%     0.0%  
    Nicolas Mahut          78.8%  32.9%  16.2%     0.2%  
    Igor Andreev           87.6%  37.3%  15.6%     0.1%  
    Oliver Golding         12.4%   1.2%   0.1%     0.0%  
    Denis Istomin          47.2%  28.3%  13.5%     0.2%  
23  Andreas Seppi          52.8%  33.2%  16.9%     0.4%  

    Player                   R64    R32    R16        W  
26  Mikhail Youzhny        50.8%  33.2%  16.3%     0.4%  
    Donald Young           49.2%  31.9%  15.3%     0.4%  
    Inigo Cervantes        20.2%   2.9%   0.4%     0.0%  
    Flavio Cipolla         79.8%  32.0%  12.5%     0.2%  
    Ryan Sweeting          79.1%  29.1%  13.8%     0.2%  
    Potito Starace         20.9%   2.8%   0.5%     0.0%  
    David Nalbandian       49.6%  33.7%  20.3%     0.9%  
8   Janko Tipsarevic       50.4%  34.4%  20.9%     1.0%  

    Player                   R64    R32    R16        W  
7   David Ferrer           70.4%  49.1%  32.6%     1.7%  
    Dustin Brown           29.6%  14.6%   6.7%     0.1%  
    Kenny De Schepper      40.5%  12.6%   5.3%     0.0%  
    Matthias Bachinger     59.5%  23.6%  11.8%     0.1%  
    Wayne Odesnik          30.0%   5.6%   1.0%     0.0%  
    Bjorn Phau             70.0%  24.3%   8.0%     0.0%  
    Jamie Baker            36.0%  22.5%   9.0%     0.1%  
30  Andy Roddick           64.0%  47.7%  25.5%     0.7%  

    Player                   R64    R32    R16        W  
19  Kei Nishikori          64.7%  52.4%  30.5%     1.9%  
    Mikhail Kukushkin      35.3%  24.7%  11.0%     0.2%  
    Andrey Kuznetsov       33.3%   5.1%   0.9%     0.0%  
    Florent Serra          66.7%  17.8%   5.3%     0.0%  
    Go Soeda               52.2%  16.7%   6.7%     0.0%  
    Igor Kunitsyn          47.8%  14.5%   5.4%     0.0%  
    Robin Haase            30.0%  16.5%   7.3%     0.1%  
9   J Del Potro            70.0%  52.3%  32.8%     2.4%  

    Player                   R64    R32    R16        W  
16  Marin Cilic            61.8%  42.7%  25.1%     1.5%  
    Cedrik-Marcel Stebe    38.2%  22.0%  10.3%     0.2%  
    Tatsuma Ito            50.0%  17.7%   6.8%     0.1%  
    Lukasz Kubot           50.0%  17.6%   6.9%     0.1%  
    Vasek Pospisil         38.9%  17.0%   7.5%     0.1%  
    Sam Querrey            61.1%  33.7%  18.5%     0.8%  
    Santiago Giraldo       42.8%  19.4%   8.9%     0.2%  
21  Milos Raonic           57.2%  30.0%  16.0%     0.5%  

    Player                   R64    R32    R16        W  
32  Kevin Anderson         53.2%  29.8%  12.7%     0.5%  
    Grigor Dimitrov        46.8%  24.9%   9.9%     0.3%  
    Albert Montanes        20.8%   4.5%   0.8%     0.0%  
    Marcos Baghdatis       79.2%  40.9%  17.6%     0.7%  
    Ivo Karlovic           39.3%   8.1%   2.5%     0.0%  
    Dudi Sela              60.7%  17.3%   7.1%     0.1%  
    Nikolay Davydenko      24.1%  13.7%   6.0%     0.1%  
4   Andy Murray            75.9%  61.0%  43.3%     7.0%  

    Player                   R64    R32    R16        W  
5   Jo-Wilfried Tsonga     93.2%  69.4%  47.0%     5.2%  
    Lleyton Hewitt          6.8%   1.2%   0.2%     0.0%  
    E Roger-Vasselin       44.5%  12.0%   4.7%     0.1%  
    G Garcia-Lopez         55.5%  17.4%   7.8%     0.1%  
    Lukas Lacko            82.2%  34.6%  12.5%     0.3%  
    Adrian Ungur           17.8%   2.4%   0.3%     0.0%  
    Jurgen Melzer          35.8%  19.6%   6.8%     0.1%  
25  Stanislas Wawrinka     64.2%  43.3%  20.8%     1.1%  

    Player                   R64    R32    R16        W  
20  Bernard Tomic          78.7%  51.6%  27.4%     1.0%  
    David Goffin           21.4%   7.3%   1.8%     0.0%  
    Jesse Levine           56.2%  24.4%  10.0%     0.1%  
    Karol Beck             43.8%  16.7%   5.9%     0.0%  
    James Ward             76.3%  28.0%  12.6%     0.1%  
    Pablo Andujar          23.7%   4.0%   0.9%     0.0%  
    R Ramirez Hidalgo       6.7%   1.1%   0.1%     0.0%  
10  Mardy Fish             93.3%  66.9%  41.3%     2.2%  

    Player                   R64    R32    R16        W  
14  Feliciano Lopez        58.5%  52.2%  28.3%     0.7%  
    Jarkko Nieminen        41.5%  35.4%  16.0%     0.2%  
    Brian Baker            33.8%   2.7%   0.2%     0.0%  
    Rui Machado            66.2%   9.8%   1.5%     0.0%  
    Matthew Ebden          58.7%  25.7%  13.2%     0.1%  
    Benoit Paire           41.3%  14.5%   6.3%     0.0%  
    Alex Bogomolov Jr.     39.8%  21.4%  11.0%     0.1%  
22  Alexandr Dolgopolov    60.2%  38.4%  23.4%     0.7%  

    Player                   R64    R32    R16        W  
27  Philipp Kohlschreiber  81.1%  52.3%  19.6%     0.9%  
    Tommy Haas             18.9%   5.9%   0.8%     0.0%  
    Jurgen Zopp            74.2%  35.2%  10.4%     0.2%  
    Malek Jaziri           25.8%   6.6%   0.9%     0.0%  
    Lukas Rosol            39.9%   9.2%   4.1%     0.1%  
    Ivan Dodig             60.1%  18.3%  10.0%     0.4%  
    Thomaz Bellucci        17.2%   7.5%   3.4%     0.1%  
2   Rafael Nadal           82.8%  64.9%  50.8%    12.0%

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Filed under Forecasting, Wimbledon

2012 Wimbledon Women’s Projections

Here are my forecasts for the Wimbledon women’s draw.  Despite Maria Sharapova’s performance at the French, my ranking system still has her third, behind both Serena and Azarenka.  Also, you might also be surprised by the significant chance I give Kim Clijsters.  While she hasn’t played much, she’s played well, and my system operates on the assumption that if someone takes the court, she is doing so fully healthy.  (Or, at least, as healthy as she’s been other times she took the court.)

If you’re interested in the rankings behind these forecasts, click here; for more background on the system, here.

I’ll post men’s odds later today, and the forecast will be updated throughout the tournament–I’ll post those links when I have them, probably mid-day Tuesday.

    Player                   R64    R32    R16        W  
1   Maria Sharapova        89.1%  68.7%  56.4%     8.7%  
    Anastasia Rodionova    10.9%   3.4%   1.2%     0.0%  
    Vesna Dolonc           21.8%   2.8%   0.9%     0.0%  
    Tsvetana Pironkova     78.2%  25.1%  15.9%     0.4%  
    Su-Wei Hsieh           51.5%  29.5%   8.0%     0.1%  
    Virginie Razzano       48.5%  27.0%   7.1%     0.1%  
    S Foretz Gacon         28.2%   7.8%   1.0%     0.0%  
29  Monica Niculescu       71.8%  35.6%   9.5%     0.1%  

    Player                   R64    R32    R16        W  
23  Petra Cetkovska        55.0%  38.3%  19.9%     0.3%  
    Vania King             45.0%  29.3%  13.8%     0.1%  
    Sloane Stephens        63.2%  23.1%   8.6%     0.0%  
    Karolina Pliskova      36.8%   9.2%   2.4%     0.0%  
    Bojana Jovanovski      58.0%  17.0%   6.8%     0.0%  
    Eleni Daniilidou       42.0%   9.9%   3.2%     0.0%  
    Petra Martic           32.6%  20.5%  10.2%     0.1%  
15  Sabine Lisicki         67.4%  52.6%  35.2%     1.2%  

    Player                   R64    R32    R16        W  
12  Vera Zvonareva         75.3%  64.2%  33.5%     2.3%  
    Mona Barthel           24.8%  16.3%   4.7%     0.0%  
    Edina Gallovits-Hall   37.5%   5.6%   0.8%     0.0%  
    Silvia Soler-Espinosa  62.5%  13.8%   3.0%     0.0%  
    Kai-Chen Chang         52.0%   7.6%   2.0%     0.0%  
    Andrea Hlavackova      48.0%   6.5%   1.5%     0.0%  
    Kim Clijsters          70.6%  63.1%  43.1%     5.4%  
18  Jelena Jankovic        29.4%  22.9%  11.4%     0.3%  

    Player                   R64    R32    R16        W  
28  Christina McHale       79.2%  64.0%  30.0%     0.7%  
    Johanna Konta          20.8%  11.0%   2.1%     0.0%  
    Lesia Tsurenko         59.4%  16.4%   3.4%     0.0%  
    Mathilde Johansson     40.6%   8.7%   1.3%     0.0%  
    Ekaterina Makarova     80.8%  38.2%  23.5%     0.6%  
    Alberta Brianti        19.2%   3.7%   1.1%     0.0%  
    Lucie Hradecka         19.1%   6.1%   2.3%     0.0%  
8   Angelique Kerber       80.9%  52.1%  36.1%     2.1%  

    Player                   R64    R32    R16        W  
3   Agnieszka Radwanska    88.4%  70.6%  50.7%     7.3%  
    Magdalena Rybarikova   11.6%   4.0%   1.1%     0.0%  
    Venus Williams         51.5%  13.3%   5.4%     0.1%  
    Elena Vesnina          48.5%  12.1%   4.8%     0.0%  
    Iveta Benesova         72.8%  35.1%  13.0%     0.3%  
    Heather Watson         27.2%   7.5%   1.4%     0.0%  
    Jamie Lee Hampton      20.8%   6.9%   1.3%     0.0%  
27  Daniela Hantuchova     79.2%  50.6%  22.3%     1.0%  

    Player                   R64    R32    R16        W  
20  Nadia Petrova          83.0%  56.0%  28.4%     0.4%  
    Maria Elena Camerin    17.0%   5.1%   1.0%     0.0%  
    Timea Babos            40.1%  13.3%   4.1%     0.0%  
    Melanie Oudin          59.9%  25.6%   9.9%     0.0%  
    Tamarine Tanasugarn    52.2%  11.9%   3.8%     0.0%  
    Anna Tatishvili        47.8%  10.1%   3.0%     0.0%  
    Camila Giorgi          18.9%  10.2%   3.6%     0.0%  
16  Flavia Pennetta        81.1%  67.9%  46.1%     1.7%  

    Player                   R64    R32    R16        W  
11  Na Li                  77.1%  60.0%  44.4%     3.8%  
    Ksenia Pervak          22.9%  11.8%   5.7%     0.0%  
    Sorana Cirstea         69.4%  22.5%  11.9%     0.1%  
    Pauline Parmentier     30.6%   5.6%   2.0%     0.0%  
    Naomi Broady           43.8%  10.4%   1.7%     0.0%  
    L Dominguez Lino       56.2%  16.1%   3.4%     0.0%  
    Alexandra Cadantu      15.1%   6.1%   0.9%     0.0%  
17  Maria Kirilenko        84.9%  67.4%  29.9%     0.6%  

    Player                   R64    R32    R16        W  
30  Shuai Peng             80.2%  54.1%  23.5%     0.4%  
    Sandra Zaniewska       19.8%   6.8%   1.3%     0.0%  
    Jarmila Gajdosova      59.4%  25.3%   7.9%     0.0%  
    Ayumi Morita           40.6%  13.9%   3.4%     0.0%  
    Arantxa Rus            52.1%  10.3%   3.7%     0.0%  
    Misaki Doi             47.9%   9.1%   3.1%     0.0%  
    Carla Suarez Navarro   17.6%   9.9%   4.0%     0.0%  
5   Samantha Stosur        82.4%  70.7%  53.1%     4.2%  

    Player                   R64    R32    R16        W  
6   Serena Williams        90.4%  80.9%  67.2%    16.1%  
    B Zahlavova Strycova    9.6%   4.6%   1.7%     0.0%  
    Johanna Larsson        43.5%   5.7%   2.0%     0.0%  
    Melinda Czink          56.5%   8.8%   3.6%     0.0%  
    Vera Dushevina         47.7%  19.1%   3.9%     0.0%  
    Aleksandra Wozniak     52.3%  22.2%   4.7%     0.0%  
    Stephanie Dubois       18.9%   5.7%   0.7%     0.0%  
25  Jie Zheng              81.1%  53.0%  16.2%     0.4%  

    Player                   R64    R32    R16        W  
19  Lucie Safarova         80.1%  57.6%  39.9%     0.8%  
    Kiki Bertens           19.9%   7.7%   2.7%     0.0%  
    Chanelle Scheepers     49.7%  17.2%   7.9%     0.0%  
    Yaroslava Shvedova     50.3%  17.6%   8.3%     0.0%  
    Laura Pous-Tio         35.9%   9.4%   2.2%     0.0%  
    Anne Keothavong        64.1%  24.9%   8.7%     0.0%  
    Coco Vandeweghe        33.8%  18.7%   6.6%     0.0%  
10  Sara Errani            66.2%  47.0%  23.7%     0.2%  

    Player                   R64    R32    R16        W  
13  Dominika Cibulkova     64.4%  53.7%  38.7%     1.5%  
    Klara Zakopalova       35.6%  26.2%  15.6%     0.1%  
    Olga Govortsova        50.6%  10.2%   3.6%     0.0%  
    Annika Beck            49.4%   9.9%   3.5%     0.0%  
    Polona Hercog          64.5%  28.1%  10.0%     0.0%  
    Kristyna Pliskova      35.5%  10.8%   2.7%     0.0%  
    Laura Robson           31.3%  15.2%   4.6%     0.0%  
24  Francesca Schiavone    68.7%  45.9%  21.3%     0.2%  

    Player                   R64    R32    R16        W  
31  A Pavlyuchenkova       64.0%  50.0%  18.4%     0.5%  
    Sofia Arvidsson        36.0%  24.0%   6.4%     0.0%  
    P Mayr-Achleitner      34.2%   6.2%   0.8%     0.0%  
    Varvara Lepchenko      65.8%  19.8%   3.9%     0.0%  
    Elena Baltacha         64.5%  10.0%   3.5%     0.0%  
    Karin Knapp            35.5%   3.5%   0.8%     0.0%  
    A Amanmuradova          9.4%   4.7%   1.4%     0.0%  
4   Petra Kvitova          90.6%  81.7%  64.8%     9.0%  

    Player                   R64    R32    R16        W  
7   Caroline Wozniacki     82.7%  71.2%  50.1%     5.0%  
    Tamira Paszek          17.3%   9.7%   3.3%     0.0%  
    Alize Cornet           55.1%  11.2%   3.3%     0.0%  
    Nina Bratchikova       44.9%   7.9%   2.0%     0.0%  
    Greta Arn              34.0%   9.8%   2.6%     0.0%  
    Galina Voskoboeva      66.0%  29.3%  11.6%     0.2%  
    Yanina Wickmayer       50.0%  30.4%  13.5%     0.3%  
32  Svetlana Kuznetsova    50.0%  30.5%  13.6%     0.3%  

    Player                   R64    R32    R16        W  
21  Roberta Vinci          81.4%  50.9%  22.1%     0.2%  
    Ashleigh Barty         18.6%   5.2%   0.9%     0.0%  
    Urszula Radwanska      48.3%  21.1%   7.0%     0.0%  
    Marina Erakovic        51.7%  22.9%   7.7%     0.0%  
    Mirjana Lucic          49.0%   8.2%   2.4%     0.0%  
    Alexandra Panova       51.0%   8.8%   2.6%     0.0%  
    Casey Dellacqua        18.1%  11.1%   4.4%     0.0%  
9   Marion Bartoli         81.9%  71.9%  53.0%     2.9%  

    Player                   R64    R32    R16        W  
14  Ana Ivanovic           67.6%  51.4%  34.2%     1.3%  
    M Martinez Sanchez     32.4%  19.7%   9.8%     0.1%  
    Kimiko Date-Krumm      31.7%   6.1%   1.8%     0.0%  
    Kateryna Bondarenko    68.3%  22.9%  10.5%     0.0%  
    Anastasiya Yakimova    49.0%  10.2%   2.2%     0.0%  
    Mandy Minella          51.0%  11.2%   2.5%     0.0%  
    Shahar Peer            37.1%  26.9%  11.1%     0.1%  
22  Julia Goerges          62.9%  51.7%  27.8%     0.6%  

    Player                   R64    R32    R16        W  
26  A Medina Garrigues     40.2%  27.6%   5.7%     0.0%  
    Simona Halep           59.8%  46.3%  12.4%     0.2%  
    Jana Cepelova          45.5%  11.2%   1.2%     0.0%  
    Kristina Mladenovic    54.5%  15.0%   1.8%     0.0%  
    Irina-Camelia Begu     34.9%   3.2%   1.0%     0.0%  
    Romina Oprandi         65.1%   9.6%   4.4%     0.0%  
    Irina Falconi           7.6%   3.4%   1.3%     0.0%  
2   Victoria Azarenka      92.4%  83.8%  72.1%    17.0%

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Filed under Forecasting, Wimbledon, WTA

Doubly Lopsided Matches

On Sunday, Novak Djokovic beat Rafael Nadal by a somewhat unusual score: 6-4 6-1 1-6 6-3.  A four-setter in the final doesn’t raise any eyebrows, but a 1-6 set … that’s a bit of a head-scratcher, especially on a fast surface.  Wimbledon is better known for server domination, which means 6-4′s, 7-5′s, tiebreaks, and the occasional 70-68.

The Djokovic-Nadal score got me curious about two questions:

  1. How often does a player lose a set 1-6 (or even 0-6) yet still win the match?
  2. How often does a player both win and lose a lopsided (6-1 or 6-0 ) set?

(Note: Yes, sometimes a 6-1 set includes only two breaks, in which case it is similar to a 6-2 set.  Yet 6-1/1-6′s are far less frequent that 6-2/2-6′s.  It would be nice to distinguish “two-break” 6-1′s from “three-break” 6-1′s, but for now, all we can do is enjoy the trivia and accept the limitations.)

Bi-directional bagels

First things first.  As we might guess, scores such as these are extremely rare at Wimbledon.  This year, the final was one of only two such matches.  The other was Xavier Malisse’s second-round win over Florian Mayer, which went in the books as 1-6 6-3 6-2 6-2.  Last year, only one Wimbledon match qualified: a first-rounder between Victor Hanescu and Andrey Kuznetsov.  Oddly enough, Hanescu dropped the third set 1-6 after splitting two tiebreaks.  In neither of these matches did the winner take his own lopsided set, as Djokovic did.

In this department, Wimbledon remains unique among the majors–it isn’t just a matter of “clay” and “everything else.”  At this year’s Australian Open, there were eight matches with 1-6 or 0-6 scores; last year there were 11.  At the 2010 US Open, there were six.  These scores are more common at the slams, because the five-set format makes it more likely that the loser of an early set (by any score) can come back to win the match.

The numbers

Last year, there were roughly 2600 tour-level matches that were played to their conclusion.  (That is, neither player retired.)   Of those, about two-thirds were straight-set victories, leaving us with 871 matches that went three sets (or five, at the slams).

Of those 871, only 94 matches contained a 1-6 or 0-6 set, and only 30 included a “lopsided” set in favor of both players, as in the Nadal-Djokovic final.  Both have been somewhat less frequent so far this year; in 1546 matches, 48 saw the winner lose a lopsided set, and 11 saw both players lose a lopsided set.  Combining the two years of data, the likelihood that any given match will include a 6-1 (or 6-0) and a 1-6 (or 0-6) is almost exactly 1 in 100.  Again, the five-set format of the slams increases the probability a bit, while the fast courts at Wimbledon have the reverse effect.

The offenders

Which players find themselves in these roller-coaster matches?  To answer that question, we have to stick with the less-specific filter of matches that include a 1-6 or 0-6 set.  If we also require a 6-1/6-0 from the winner, there isn’t enough data to make things interesting.

One might guess that the strongest servers would be far down the list, while counterpunchers populate the top.  That isn’t the case.  The players who are known for mental lapses–regardless of their serving and returning skills–seem to dominate the upper tier.

Looking at all tour-level matches from 2007 through last week, we find that Andy Murray takes the cake.  He has played in 18 of these matches, dropping a lopsided set in 10 of his victories, while winning a lopsided set in 8 of his losses.  Murray is in a class by himself–number two on the list is Guillermo Garcia-Lopez, at 13.  In third place is Djokovic, with 12 (he is 8-4 in such matches), though the Wimbledon final was the only occurence so far in 2011.

Twelve men are clustered at 10 and 11 of these matches, and the list features a lot of Frenchmen, and several other players known for questionable mental strength:

  • 11: Julian Benneteau, David Ferrer, Fabio Fognini, Fernando Verdasco
  • 10: Thomaz Bellucci, Mardy Fish, Richard Gasquet, Paul-Henri Mathieu, Phillipp Petzschner, Tommy Robredo, Radek Stepanek, Jo-Wilfried Tsonga

Of these, Fognini (9-2) and Tsonga (8-2) have the dubious honor of winning the most matches–that is, they are on the list because they drop lopsided sets in matches that they win.  Mathieu (2-8) is at the other extreme, dominating sets in the middle of losses.

The Wimbledon final was a rarity for Nadal–it was only the fourth time he’d been involved in a match with this sort of score, and it was only the second time he won a lopsided set in the middle of a loss.  Roger Federer has only played in three such matches.

We probably can’t read too much into these numbers, but it is interesting to see so many of the same types of players show up at the top of a list.  At the very least, we’ve learned that the 1-6 set in Sunday’s final was quite rare, and the 6-1 1-6 sequence was even rarer.

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Filed under Research, Wimbledon

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.

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Filed under Forecasting, Wimbledon

Wimbledon Round 1: Qualifers and Other Underdogs

Some people watch the opening rounds of majors to see the top players drub lesser competition, perhaps gauging fitness by just how badly, say, Roger Federer beats Mikhail Kukushkin.  I get much more enjoyment out of the matches on Court 15, between players who are almost certainly not going to be around a week from now.

Last week’s qualifying rounds gave us a great group of contenders, plus another five lucky losers.  Wimbledon is also fairly unique in giving a handful of its eight wild cards to non-local players, giving a few free spots to players with good track records at the tournament (Arnaud Clement, Alejandro Falla) or guys on recent hot streaks (Dudi Sela).  Taken together, there are dozens of good early-round matches that can be enjoyed without the slightest reference to the thankfully-concluded Isner-Mahut first-rounder.

Let’s go to the bullet points:

  • Perhaps the biggest upset of the first round was Bernard Tomic’s straight-set win over Nikolay Davydenko.  Tomic is on the way up, and it’s ever more apparent that Davydenko is on the way out.  Tomic will next play Igor Andreev, who needed five sets to get past Teymuraz Gabashvili.
  • “Upset” may not be the right word, but I was somewhat surprised that Lleyton Hewitt was healthy enough to play today, let alone to beat Kei Nishikori.  The Aussie shouldn’t have much of a chance against Robin Soderling, but then again, Soderling’s performance was one of the weakest in the first round among the top seeds.
  • Grega Zemlja was one of two lucky losers to reach the second round; he beat Lucas Lacko to do so.  Lacko has been a bit of a mystery; he has posted a handful of solid wins in the last few years, but he hasn’t been able to stick in the top 100.  This was a big opportunity to get into a slam, and he let it go by.
  • The other very-lucky lucky loser was Ryan Harrison, who handled Ivan Dodig in straight sets.  Harrison bagelled the Croatian in the second, reeling off 25 of 33 points.  Depending on how some other lowly-ranked players do this week, the win might move Harrison into the ATP top 100.  His second-rounder against David Ferrer should be fun to watch, even if the conclusion is a given.
  • Frenchman Kenny De Schepper is ranked outside of the top 200, but he gave Olivier Rochus a real test today, pushing the Belgian to five sets.  My algorithm didn’t give De Schepper much credit, but apparently he didn’t check the numbers before heading out on court today.
  • Dudi Sela, in on a WC this year after stringing together some challenger titles this spring, had an easy first-rounder against Frederico Gil.  Gil always seems to be an easy match for somebody at a slam, yet he never leaves the top 100 for long.
  • Marinko Matosevic missed a big opportunity, falling to Juan Ignacio Chela without much of a fight.  Matosevic has a one-dimensional game, but when that dimension is a serve, a player still has a chance at the AEC.  Now the pressure is on Alex Bogomolov, another lower-ranked player who my algorithm favors over Chela.
For even more Wimbledon, check out the new Tennis Tracker at the Wall Street Journal website.  It gives real-time updates for about 20 top ATP and 20 top WTA players, including some win probabilities and a few stats, crunched by yours truly.

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Filed under Daily recaps, Wimbledon