Is Roger Federer the Olympic Favorite?

Roger Federer is back at #1.  Is he the best player in the game right now?  More immediately, is he the favorite when the world’s best return to Wimbledon for the Olympics?

In theory, the world number one should be the favorite, especially on the favorite’s preferred surface.  Especially a few weeks after winning a grand slam at the same venue.  Yet there is nothing like a consensus: Bettors are generally giving a slight edge to Novak Djokovic, the same man who lost to Federer only a couple of weeks ago.  My ranking system also gives the edge to Djokovic.

How is the world number one not number one?  Two issues are in play here.  First, Federer, at nearly 31 years of age, can’t be expected to keep playing like he did two weeks ago, or like he did last fall.  Second, the Olympic draw isn’t likely to substantially affect Djokovic’s chances, but it could cast serious doubt on Federer’s.

For any player, and especially for a thirty-something, past results are no guarantee of future performance.  ATP rankings are based entirely on past results, some nearly one year old, weighted as if they happened yesterday.  Considering Federer’s career as an arc, with 2012 doubtless located on the downslope, Wimbledon looks more like an aberration than a rebirth.  Repeated losses earlier in the year to Djokovic and hiccups against Tommy Haas and Andy Roddick, not to mention a near-disaster against Julien Benneteau, may tell us more than a couple of strong wins against Djokovic and Murray.

This isn’t to say Federer can’t win the gold medal.  But he wasn’t the favorite going into Wimbledon, and aside from the order of the ATP rankings, not much has changed since then.

Still lurking are many men who could upset Roger, and that’s where the draw comes in.  Before the Olympic draw is released, we need to remember all the players Federer didn’t have to beat en route to his seventh Wimbledon title.  His fourth round and quarterfinal opponents were Xavier Malisse and Mikhail Youzhny, players who would make more sense as Fed’s second and third round victims.

Federer has lost to three active players in his Wimbledon career: Rafael Nadal, Tomas Berdych, and Jo-Wilfried Tsonga.  Other recent losses on hard courts: Haas, Roddick, and John Isner.  He could’ve drawn at least one, or as many as three of those guys at Wimbledon instead of a lineup of journeymen.  His Olympic draw may not be so fortunate.

Djokovic, on the other hand, doesn’t have much of anyone to fear.  His chances (real or perceived) of Olympic gold won’t change much if Berdych or Tsonga shows up in his quarter.

But to say that Federer is not the favorite doesn’t mean that Novak is an overwhelming one.  He gets that honor almost by default.  For the first time in what seems like years, we’re entering a major event without a clear frontrunner.  Everyone’s flaws have been exposed.  Nadal crashed out of Wimbledon and hasn’t won a hard-court event since 2010.  Federer’s dominance and health both eluded him in Wimbledon’s middle rounds.  Djokovic’s aura of streak-inspired invincibility is long gone.

One of these three men will probably take home the gold.  But pick one, and your man is likely to disappoint you.

Update: A couple of hours after I posted this, Nadal withdrew. That betters the chances of Federer and Djokovic.  The other winner is David Ferrer, who gets a top four seed.  That’s no cakewalk to the semis with such a deep draw, but it’s certainly easier than needing to beat one of the big four just to get to the bronze medal match.

A Friendly Reminder About Milos Raonic

It’s been an exciting couple of months in tennis, and Milos Raonic has gotten lost in the shuffle.  He hasn’t beaten a top-40 player since Barcelona in April, and he remains outside of the top 20.  His five-set battle at Wimbledon against Sam Querrey was a high-class, hard-fought effort on both sides, and naturally, Querrey got most of the press after that one.

This isn’t a sophomore slump.  It’s the calm before the storm.

Much like Juan Martin del Potro, Raonic has been stunted in 2012 by the juggernaut that has been Roger Federer.  Of Raonic’s 10 losses this year, three have come against Roger, and all three have gone three sets, two of them to a third-set breaker.

More importantly, Raonic’s ranking doesn’t tell the whole story.  The Canadian missed almost the entire second half of 2011, coming back after the US Open at half-strength.  He has almost no points to defend between now and the end of the year.

Even a modest projection for Raonic suggests that he’ll move into the top 16 by the end of the year.  And as he climbs the ladder, he’ll get better seedings, avoiding roadblocks like Federer in the 2nd round of Madrid.

If all Milos does is play up to his seed at this year’s three remaining Masters, reach the third round of the US Open, and defend his semifinal points from Stockholm, he’ll ascend to approximately #17 in the rankings.  (I’ve ignored the Olympics, since that event will inflate almost everyone’s point total.  Raonic may also further pad his total this week at Newport.)  One decent run, like a Masters quarterfinal, a 500 semifinal, or even a fourth-round finish in Flushing, puts him at the edge of the top 15.  Based on his skill level, that’s where he belongs right now.

And that conservative path is almost certainly not all that the Canadian will accomplish.  On paper, his grass season is a bit disappointing, but a three-set loss to Federer and the five-setter against the resurgent Querrey is hardly a disaster.  And Raonic’s clay season exceeded expectations, including wins over Nicolas Almagro, Andy Murray, and David Nalbandian.

A couple of rough draws have made it easy to forget about one of the game’s future stars.  Don’t be surprised to see him persistently climbing the rankings, pushing aside top-tenners, for the rest of the 2012 season.

 

How Underdogs Could Win Wimbledon Doubles

Italian translation at settesei.it

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.

New Functionality (and a little more data) on TennisAbstract.com

Since meta-sizing the TennisAbstract.com database, I’ve focused on making each player page more useful and functional.  Here are the highlights:

  • “Show Career” link.  By default, most pages show a player’s matches for the last 52 weeks.  When you start applying filters, the number of matches shrinks heavily.  In many cases, what you really want is that set of filters applied to the player’s whole career. Now, there’s a “show career” link at the bottom of every list of matches.  (Unless, of course, the table is already displaying the full career!)
  • Apply filters to a new player.  A few days ago, a reader sent me this link, carefully constructed from four different filters, with four selections in one of the filters.  Once you’ve done all that work, what if you want to see the same filters applied to a different player?  Now, instead of repeating the process, enter the player into the search box below the match list, and voila! the filters are applied to another player.
  • Ctrl-Click for multiple selections. I’ve never liked the behavior of the menus for multiple selections.  By default, if you clicked on a second selection, the page assumed you wanted both.  For instance, to change from “hard” to “clay,” for instance, you had to click on “clay,” then click on “hard” again to deselect “hard.”  Now, these selections behave more intuitively.  If you want multiple selections, hold down the control key while clicking on a filter.
  • More filters for best-of-five matches.  If you haven’t looked recently, check out the menus for “Results” and “Sets.”  Now you can search for five-setters where a player lost the first two sets, or best-of-fives that went exactly four sets, and so on.  By splitting these into two menus, there are more possible queries than ever.
  • Exclude players from results.  How impressive are Juan Martin del Potro’s results this year if you exclude his losses to Roger Federer?  The “Exclude Opp” menu makes it easy to answer questions like these.
  • More Davis Cup coverage.  I’ve added World Group Playoffs (or Challenge Rounds, or Qualifying Rounds, or whatever they called them from one year to the next) from 1981 to 2002.  Now World Group results are complete back to 1981, along with Group 1 and Group 2 back to 1993.  Still more to come.

Many of these came directly from reader suggestions.  I do appreciate every comment even though I can’t act on them all.  Many suggestions aren’t yet reflected on the site, but are moving their way up the to-do list.   Let me know what would make the site more valuable to you.

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.

The Greatest Upset in Sports Recency

Italian translation at settesei.it

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.

The Aging Wimbledon Men’s Draw

Italian translation at settesei.it

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.

Continue reading The Aging Wimbledon Men’s Draw

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%

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%