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

The Effect of Serve Speed

Italian translation at settesei.it

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%

Quantifying Comebacks and Excitement With Win Probability

Italian translation at settesei.it

As promised the other day, there’s a lot we can do with point-by-point and win probability stats for over 600 grand slam matches.

I’ve beefed up those pages a bit by borrowing some ideas from Brian Burke at Advanced NFL Stats.  He invented a couple of simple metrics using win probability stats to compare degrees of comebacks and the excitement level of (American) football games.

The concepts transfer to tennis quite nicely.  Comeback Factor identifies the odds against the winner at his lowest point.  I’ve defined it the same way Burke does for football: CF is the inverse of the winning player’s lowest win probability.  In the US Open Federer/Djokovic semifinal, Djokovic’s win probability was as low as 1.3%, or 0.013.  Thus, his comeback factor is 1/.013, or about 79.  That’s about as high a comeback factor as you’ll ever see.

On the other end, comeback factor cannot go below 2.0 — that’s the factor if the winning player’s WP never fell below 50%.  Matches in which the winner dominated are often very close to 2.0, as in the Murray/Nadal semifinal.  In that match, Nadal’s low point was facing a single break point at 2-3 in the first set; the comeback factor is 2.3.

A good way to think about comeback factor is this: “At his lowest point, the winning player faced odds of 1 in [CF].”

Excitement Index is a measure of volatility, or the average importance of each point in a match.  “Volatility” measures the importance of each individual point; EI is the average volatility over the course of a match.

(Burke sums the volatilities, reasoning that in football, a fast-paced game with many plays is itself exciting.  Since there is no clock in tennis [not exactly, anyway], it seems appropriate to average the volatilities.  Win probability already considers the excitement and importance of a deciding final set.)

At the moment, I’m calculating EI by multiplying the average volatility by 1000.  The Murray/Nadal match is 35 (not very exciting, though Murray fought back), the Djokovic/Federer match is 47 (more on that in a minute), while the 2nd rounder between Donald Young and Stanislas Wawrinka is 64.  I haven’t looked at all the matches yet, but EI should generally fall between 10 and 100, possibly exceeding 100 in rare instances like the Isner/Mahut marathon.

It seems like Djok/Fed should be higher, perhaps because we remember the excitement of the final set.  (And it may be that the final set should be weighted accordingly.)  But looking at the match log, there were an awful lot of quick games, which translate to relatively low volatility.  By contrast, Donald/Stan was more topsy-turvy throughout, as the players traded sets, then send volatility through the roof with a pair of breaks midway through the final set.

Both EI’s scaling and its exact definition are works in progress.  When I get a chance, I’ll do a survey of matches for which I have point-by-point data to further investigate both of these new (to tennis) metrics.

Win Probability Graphs and Stats

Win probability graphs and stats are now available for over 600 grand slam matches from 2011.  Thanks to IBM Pointstream from this year’s slams, there is a wealth of data available like never before.

Here’s the main menu.

Here’s a sample match: The US Open semifinal between Federer and Djokovic.

When I first started publishing tennis research, win probability was one of my focuses.  You can find earlier work here, which links to specific tables for games, sets, and tiebreaks.  I’ve also published much of the relevant code, which is written in Python.

Win probability represents the odds of each player winning after every point of the match, based on the score up to that point and which player is serving. It makes no assumptions about the specific skill levels of each players, but does assume that the server has an advantage, which varies based on surface and gender.  With every point, each player’s win probability goes up or down, and the degree to which it rises or falls is dependent on the importance of the point–at 4-1, 40-0, winning the point is nice, but losing the point just delays the inevitable; at 5-6 in a tiebreak, the potential change in win probability is huge.

To quantify that in the graphs, I show another metric: Volatility, which measures the importance of each point. It is equal to the difference in win probabilities between the server winning and losing the following point. 10 percent is exciting, 20 percent is crucial, and 30 percent is edge-of-your-seat stuff.

Assumptions

To produce these numbers, I needed to make several simplifying assumptions.  Some are more important than others; here are the big two:

  • The players are equal.
  • Each player’s ability does not vary from point to point.

The first of these is almost always false, and the second is probably false as well.  The first, however, makes things more interesting.  In most matches Novak Djokovic plays these days, he goes in with an 80-percent-or-better chance of winning.  If we graphed one of his matches starting at 85 percent, we’d usually get a very slowly ascending line.  Instead, by starting at 50 percent, we can see where he and his opponent had their biggest openings, and who took advantage.

(In this long-ago post, I showed a sample graph with an assumption similar to the 85 percent for Djokovic, and you can see some of what I mean.)

Assuming that the players are equal also sidesteps of messy question of how to quantify each player’s skill level on that day, on that surface, against that opponent.

The second big assumption ignores possibility real-world attributes like clutch performance and streakiness, along with more pedestrian considerations like some players’ stronger serving in the deuce or ad court.

Another long-ago article of mine suggests that servers are not absolutely consistent, possibly because of natural rises and falls in performance, also possibly because of risk-taking (or lack of concentration) in low-pressure situations.  One of the most interesting directions for research with these stats is into this inconsistency: We need to figure out whether some players are more consistent than others, whether “clutch” exists in tennis, and much more.

One more set of assumptions regards the server’s advantage.  Since these graphs only encompass the four grand slams, I set the server’s win percentage for each tournament.  The numbers I used for men are: 63% in Australia, 61% at the French, 66% at Wimbledon, and 64% at the U.S. Open.  I used percentages two points lower for women at each event.

More on Win Probability

There’s very little out there on win probability and volatility in tennis.  I wasn’t the first person to work out the probability of winning a game, a set, or a match from a given score, but as far as I know, I’m the only person publishing graphs like this.  Much of the problem is the limited availability of play-by-play descriptions for professional tennis.

That problem doesn’t apply to baseball, where win probability has thrived for years.  Here’s a good intro to win probability stats in baseball, and fangraphs.com is known for its single-game graphs–for instance, here’s tonight’s’s Brewers game.  In many ways, win probability is more interesting in baseball than in tennis.  In tennis, there are only two possible outcomes of each point, while in baseball, there are several possible outcomes of each at-bat.

Enjoy the graphs and stats!

The Speed of Every Surface

Italian translation at settesei.it

Last week, I wrote an article for the Wall Street Journal noting the relatively slow speed of this year’s U.S. Open.  It’s not clear whether the surface itself is the cause, or whether the main factor is the humidity from Hurricane Irene and Tropical Storm Lee.  For whatever reason, aces were lower than usual, creating an environment more favorable to, say, Novak Djokovic than someone like Andy Roddick.

The limited space in the Journal prevented me from going into much detail about the methodology or showing results from tournaments other than the slams.  There’s no word limit here at Heavy Topspin, so here goes…

Aces and Server’s Winning Percentage

Surface speed is tricky to measure–as I’ve already mentioned, “surface speed” is really a jumble of many factors, including the court surface, but also heavily influenced by the atmosphere and altitude.  (And, possibly, different types of balls.)  If you were able to physically move the clay courts in Madrid to the venue of the Rome Masters, you would get different results.  But teasing out the different environmental influences is little more than semantics–we’re interested in how the ball bounces off the court, and how that affects the style of play.

So then, what stats best reflect surface speed?  Rally length would be useful, as would winner counts–shorter rallies and more winners would imply a faster court.  But we don’t have those for more than a few tournaments.  Instead, I stuck with the basics: aces, and the percentage of points won by the server.

Important in any analysis of this sort is to control for the players at each tournament.  The players who show up for a lower-rung clay tournament are more likely to be clay specialists, and the men who get through qualifying are more likely to be comfortable on clay.  Also, the players who reach the later rounds are more likely to be better on the tournament’s surface.  Thus, the number of aces at, say, the French Open is partially influenced by surface, and partially influenced by who plays, and how much each player plays.

Thus, instead of looking at raw numbers (e.g. 5% of points at Monte Carlo were aces), I took each server in each match, and compared his ace rate to his season-long ace rate.  Then I aggregated those comparisons for all matches in the tournament.  This allows us to measure each tournament’s ace rate against a neutral, average-speed surface.

The Path to Blandness

The ace rate numbers varied widely.  While the Australian Open and this year’s US Open were close to a hypothetical neutral surface speed, other tourneys feature barely half the average number of aces, and still others have nearly half-again the number of aces of a neutral surface.   I’ve included a long list of tournaments and their ace rates below; you won’t be surprised to see the indoor and grass tournaments on the high end and clay events at the other extreme.

But there’s a surprise waiting.  I also calculated the percentage of points won by the server, and like ace rate, I controlled for the mix of players in every event.  While ace rate varies from 53% of average to 145% of average, the percentage of points won by the server never falls below 90% of average, rarely drops below 95%, and never exceeds 105%.  53 of the 67 tournaments listed below fall between 97% and 103%–suggesting that surface influences the outcome of only handful of points per match.

That may defy intuition, but think back to the mix of players at each tournament.  Big-serving Americans don’t show up at Monte Carlo, while South Americans generally skip every non-mandatory event in North America.  The nominal rate at which servers win points varies quite a bit, but that’s because of the players in the mix.

Also, this finding suggests that, as a stat, aces are overrated.  They may be a useful proxy for server dominance–if a players hits 15 aces in a match, he’s probably a pretty good server–but they come nowhere near telling the whole story.  Aces on grass turn into service winners on hard courts, and then become weak returns and third-shot winners on clay.  The end result is usually the same, but Milos Raonic is a lot scarier when the serves bounce over your head.

Finally, it would be a mistake to say that a variance of 3-5% in serve points won is meaningless.  It may be less than expected, but especially between good servers, 3-5% can be the difference.  Move Saturday’s Federer/Djokovic semifinal to a surface like Wimbledon’s, and we’d be looking at a different champion.

All the Numbers

Here is the breakdown of ace rate and serve points won, compared to season average, for nearly every current ATP event.

Since I am using each season’s average, you may wonder whether the averages themselves have changed from year to year.  I’ve read that courts are getting slower, but in the five-year span I’ve studied here, the ace rate has actually crept up a tiny bit.  Each tournament varies quite a bit–probably due to weather–but generally ends up at the same numbers.

Below, find the 2011 ace rate and percentage of serve points won, as well as the average back to 2007.   Again, these are controlled for the mix of players (including how much each guy played), and the numbers are all relative to season average.

The little letter next to the tournament name is surface: c = clay, h = hard, g = grass, and i = indoor.

Tournament          2011Ace  2011Sv%    AvgAce  AvgSv%  
Estoril          c    57.5%    96.6%     53.3%   94.3%  
Monte Carlo      c    52.0%    92.1%     53.9%   91.2%  
Umag             c    58.6%    95.2%     58.7%   94.3%  
Serbia           c    54.2%    93.5%     61.0%   94.8%  
Rome             c    62.5%    95.9%     62.9%   94.4%  
Buenos Aires     c    61.9%    99.0%     62.9%   98.6%  
Houston          c    64.9%    97.2%     66.6%   96.8%  
Valencia         i                       68.0%   96.4%  
Barcelona        c    55.7%    94.3%     68.0%   96.2%  
Dusseldorf       c    45.7%    96.5%     72.8%   97.2%  

Hamburg          c    78.0%    96.6%     74.3%   96.4%  
Bastad           c    63.8%    94.5%     76.8%   97.7%  
Roland Garros    c    78.0%    98.4%     77.1%   97.5%  
Santiago         c    84.5%    98.5%     81.5%   99.4%  
Costa do Sauipe  c    83.4%   101.7%     84.2%   98.9%  
Nice             c    88.5%    97.4%     84.3%   98.1%  
Casablanca       c    79.1%    99.0%     84.9%   98.2%  
Acupulco         c    70.9%    95.6%     86.0%   98.7%  
Madrid           c    77.0%    98.5%     86.1%   98.0%  
Munich           c    87.9%   100.1%     86.5%  100.0%  

Beijing          h                       86.7%   97.3%  
Los Angeles      h    84.7%    97.2%     87.7%   97.3%  
Kitzbuhel        c    95.8%    97.9%     89.0%   98.6%  
Toronto          h                       89.6%   98.3%  
Chennai          h    82.3%    98.0%     89.6%   98.7%  
Stuttgart        c    77.0%    95.8%     89.7%   98.1%  
Indian Wells     h    88.9%    99.0%     90.9%   98.0%  
Doha             h   125.5%   101.9%     91.2%   97.6%  
Auckland         h   103.1%   102.0%     93.9%   98.7%  
Miami            h    94.5%    97.9%     94.4%   98.0%  

Shanghai         h                       94.6%   98.1%  
Australian Open  h    97.6%    97.3%     96.5%   96.9%  
Kuala Lumpur     h                       97.1%   97.3%  
Sydney           h   105.8%   100.0%     97.4%   99.1%  
St. Petersburg   i                       97.8%  101.7%  
Montreal         h    91.3%    98.4%     98.1%   98.2%  
Delray Beach     h   106.2%    99.9%     99.1%   98.6%  
Gstaad           c   104.5%   100.1%    101.2%  101.4%  
Dubai            h   102.7%    96.5%    103.2%   98.2%  
US Open          h   101.3%    97.4%    104.0%   98.7%  

Vienna           i                      105.8%  101.4%  
Johannesburg     h   110.0%   102.7%    106.0%  101.0%  
Washington DC    h    97.5%   100.1%    106.8%   99.8%  
Newport          g    93.3%    99.0%    107.5%  101.7%  
Winston-Salem    h   108.1%    99.6%    108.1%   99.6%  
Atlanta          h   110.0%   100.9%    108.4%   99.0%  
Bangkok          h                      110.5%  101.6%  
Cincinnati       h    96.2%    98.9%    111.7%  100.5%  
Zagreb           i   107.0%    99.2%    112.3%  102.3%  
Moscow           i                      113.0%  101.3%  

Brisbane         h   130.6%   100.3%    113.4%  100.0%  
Eastbourne       g   111.2%   101.8%    114.1%  102.9%  
Paris Indoors    i                      115.4%   99.6%  
Rotterdam        i   123.8%   103.7%    115.9%  101.0%  
Basel            i                      117.7%  101.3%  
San Jose         i   108.6%   103.0%    120.0%  102.7%  
Wimbledon        g   119.4%   102.8%    120.7%  103.0%  
Queen's Club     g   113.3%   101.8%    121.5%  103.2%  
Halle            g   122.9%   104.7%    123.2%  102.5%  
Marseille        i   127.4%   102.8%    124.2%  102.2%  

Stockholm        i                      124.4%   99.8%  
Metz             i                      124.6%  101.7%  
Tokyo            h                      124.7%  100.5%  
s-Hertogenbosch  g   110.9%   102.1%    126.3%  104.0%  
Memphis          i   117.1%   101.2%    129.1%  102.0%  
Montpellier      i                      145.4%  104.5%

US Open Qualifying: Friday Notebook

On Friday, I saw parts of eleven matches, including many of the men who ended up qualifying for the main draw.  Since I’ve already written about many of these players (Tuesday notebook; Wednesday notebook), I’ll keep this brief.

Joao Souza vs David Goffin

Souza, a 23-year-old Brazilian, is a big guy with a big game.  He plays as explosively as anyone I’ve ever seen in qualifying, reminding me of Jo-Wilfried Tsonga in his ability to impose his will on a match.  Goffin is a steady counterpuncher and was holding his own until Souza just exploded near the end the first set, hitting untouchable serves and forehands to seal the set.  Judging from his track record so far, Souza doesn’t reliably play at that level, but there’s no doubt the talent is there.

Goffin is a very different player.  I saw parts of his second and third-round matches yesterday, and was impressed both times.  He has a very slight build and is only 20, suggesting that more power is on the way.  Now, he looks like Gilles Simon’s 12-year-old brother, and plays quite a bit like Simon, moving very well around the court and hitting solid groundstrokes off of both wings.  The 7-5 7-5 loss to Souza notwithstanding, he seemed mentally strong, holding his own against physically superior players.

Sergei Bubka vs Rogerio Dutra da Silva

I’m afraid I can’t explain Bubka’s superiority here, since I only saw the second set, which Dutra da Silva won 6-4.  The Brazilian is skinny but hits like a big man, finding impressive angles to crush forehand winners.  Like Souza, he has the talent to simply bowl someone over, and Bubka didn’t have the defensive skills to stop him.  But this time, Dutra da Silva didn’t maintain that level.

I wish I had more to say about Bubka, but he didn’t do much to impress.  He hits hard and is reasonably steady, which was apparently enough against the mercurial Brazilian.

Augustin Gensse vs Laurynas Grigelis

I wrote about Grigelis on Wednesday.  He crushed Adam Kellner, though he let up a bit near the end the first set.   It would appear that his mind is holding him back; he sputtered to a 6-1 7-6 win over a weak 2nd-round opponent in Bastian Knittel.  In two tight sets on Friday against Gensse, he couldn’t close it out.

Gensse is a typical qualifying journeyman, hitting reasonably big shots without quite the consistency necessary to reach the next level.  I also saw the end of his 2nd-round victory over Joao Sousa (not Souza, mentioned above), and held on while Sousa’s game simply collapsed.

Grigelis may have more potential than any other player in this year’s qualifying tournament, so it’s a shame to see him fail to convert on the opportunity to make his first US Open main draw.

Louk Sorenson vs Gastao Elias

I saw the Irishman in his 2nd-rounder, which started bright and early at 10am.  When I arrived, Elias was dictating play, forcing Sorenson to play clay-court tennis.  Elias looked good, with steady topspin groundstrokes and the ability to recover from defensive positions.  Sorenson was too erratic to do anything with that.

But near the end of the first set, everything changed, and I’m not sure how.  Elias started missing, Sorenson’s forehand came alive, and Louk was up a break in the second set before Elias knew what had happened.  Elias–still only 20–has the brighter future of the two–especially on clay–but Sorenson came through when it mattered.  He came back later in the day to beat Martin Fischer in straight sets and qualify for the main draw.

Vasek Pospisil vs Charles-Antoine Brezac

This was another second-rounder.  I wrote about both players earlier this week, and was excited to see Pospisil take on a stronger opponent.  The second set lived up to my expectations, with one deuce game after another before the Canadian finally broke for a 6-1 7-5 victory.

20 years old and about 6’4″, it’s no surprise that Pospisil isn’t yet in full control of his strokes.  He wasn’t as consistent as the veteran Brezac, but there’s no questioning his vast potential.  The serve is huge, and his awareness of the court is far beyond his years.  He didn’t come in as often as he could have, but neither did Brezac, so he got away with it.  Pospisil came back out to beat another journeyman, Grega Zemlja, to qualify for the main draw.

Malek Jaziri vs Guillaume Rufin

Rufin retired two games after I arrived, so I suspect anything I observed about him isn’t very accurate.  Jaziri got lucky with that outcome, even though he was up a set at the time.  The Tunisian is a clay-courter, and he didn’t make any concession to the hard surface in yesterday’s match.  He had the loopiest groundstrokes I saw all week, and I’m not sure I saw him hit a single winner.  He drew Thiemo de Bakker in the first round, one of the few possibilities that might get him a main draw win.

Conor Niland vs Matwe Middelkoop

Niland was a mess in the first set, losing 6-2 on the “strength” of a backhand that went everywhere but inside the lines.  Throughout the match, he displayed one of the least consistent two-handed backhands I’ve ever seen, hitting flat, topspin, and extreme topspin shots with more or less the same motion.

After a bathroom break between sets, Niland came back out a different man.  The backhand didn’t win him points, but it stopped losing them, and got him in position for several inside-out forehand winners.  Middelkoop got lulled into passivity by winning the first set, and didn’t recover before Niland took control of the match.  Unfortunately for Conor, his backhand will be exposed about two games into his first-rounder with Novak Djokovic.

Romain Jouan vs Denis Kudla

Kudla was simply overmatched.  (I also wrote about his win on Tuesday.)  At this stage in his young career, the young American is basically a counterpuncher, occasionally going on offense with an exceptional backhand.  From the beginning of the match, Jouan looked erratic, but he was consistent enough to overpower Kudla again and again.  As was the case earlier this week, Kudla didn’t display the best tactics, going for down-the-line backhands from defensive positions.  Jouan put him on defense all the time, and it was clear that the American didn’t have an answer for it.

Like Souza, Jouan is a powerful, imposing player.  Against a better opponent, it’s questionable whether he can maintain that level, and he’ll likely be demolished by Tomas Berdych next week.

A few more notes on qualifying winners:

I’ve seen Michael Yani several times, and written about him here and here.  Same with Marsel Ilhan: here.  Jesse Huta Galung is a very stylish, smooth player who has never gotten the results I think he deserves–I don’t think he consistently serves and volleys, but he has the talent to do so.  I wrote about Go Soeda earlier this week.  When I saw Robert Farah a few years ago, he was an electrifying, inconsistent player, with an odd mix of flat, hard groundstrokes and clay-court tactics.

US Open Women’s Draw Predictions

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

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

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

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

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

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

US Open Men’s Draw Predictions

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

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

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

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

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

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

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

WTA Hard Court Rankings, pre-US Open

According to my ranking algorithm, Serena Williams is best player headed into the 2011 US Open.  It isn’t even close. Keep in mind that my system is focused specifically on who can beat whom on hard courts, not on a nebulous sense of the “best, most consistent player.” Serena may not be likely to show up at any given tournament, but when she does, she wins.

Thanks to a solid grass-court season and her win in Montreal, Serena is well ahead of the pack.  Kim Clijsters still has a lock on the #2 spot, but she won’t be in New York.  Wozniacki, Azarenka, and Sharapova are very tightly packed in the next three spots, in that order.

Despite playing even less than her sister has, Venus Williams comes in at #14.  Fans of American tennis will find some promise here–my system favors Melanie Oudin (58), Vania King (75), and Sloane Stephens (81), while all are currently outside the WTA top 100.

Here is the full list.  Check back later this weekend for tournament predictions based on these rankings and the full draw.

RANK  PLAYER                        PTS  
1     Serena Williams              8504  
2     Kim Clijsters                6683  
3     Caroline Wozniacki           6307  
4     Victoria Azarenka            6178  
5     Maria Sharapova              6158  
6     Petra Kvitova                5846  
7     Vera Zvonareva               5698  
8     Samantha Stosur              4547  
9     Na Li                        4528  
10    Agnieszka Radwanska          4379  
11    Marion Bartoli               4152  
12    Andrea Petkovic              3862  
13    Dominika Cibulkova           3704  
14    Venus Williams               3589  
15    Sabine Lisicki               3409  
16    Shuai Peng                   2978  
17    Ana Ivanovic                 2918  
18    Daniela Hantuchova           2887  
19    Svetlana Kuznetsova          2841  
20    Jelena Jankovic              2737  

RANK  PLAYER                        PTS  
21    Alisa Kleybanova             2500  
22    Anastasia Pavlyuchenkova     2494  
23    Flavia Pennetta              2475  
24    Roberta Vinci                2253  
25    Yanina Wickmayer             2246  
26    Maria Jose Martinez Sanchez  2212  
27    Nadia Petrova                2209  
28    Francesca Schiavone          2139  
29    Shahar Peer                  2097  
30    Jie Zheng                    2084  
31    Julia Goerges                1812  
32    Lucie Safarova               1746  
33    Galina Voskoboeva            1735  
34    Kaia Kanepi                  1731  
35    Ekaterina Makarova           1727  
36    Maria Kirilenko              1718  
37    Tsvetana Pironkova           1597  
38    Elena Vesnina                1387  
39    Kateryna Bondarenko          1364  
40    Christina McHale             1318  

RANK  PLAYER                        PTS  
41    Petra Cetkovska              1295  
42    Gisela Dulko                 1279  
43    Iveta Benesova               1234  
44    Tamira Paszek                1227  
45    Klara Zakopalova             1195  
46    Dinara Safina                1189  
47    Bethanie Mattek-Sands        1146  
48    Aravane Rezai                1133  
49    Virginie Razzano             1111  
50    Anastasija Sevastova         1098  
51    Jarmila Gajdosova            1092  
52    Sara Errani                  1076  
53    Vera Dushevina               1063  
54    Petra Martic                 1052  
55    Alona Bondarenko              983  
56    Marina Erakovic               965  
57    Bojana Jovanovski             964  
58    Melanie Oudin                 947  
59    Anna Chakvetadze              928  
60    Magdalena Rybarikova          901  

RANK  PLAYER                        PTS  
61    Alize Cornet                  891  
62    Simona Halep                  885  
63    Timea Bacsinszky              863  
64    Polona Hercog                 823  
65    Greta Arn                     823  
66    Aleksandra Wozniak            816  
67    Ksenia Pervak                 810  
68    Romina Oprandi                800  
69    Kimiko Date-Krumm             780  
70    Alexandra Dulgheru            772  
71    Jelena Dokic                  767  
72    Elena Baltacha                754  
73    Barbora Zahlavova Strycova    753  
74    Agnes Szavay                  751  
75    Vania King                    709  
76    Ayumi Morita                  708  
77    Angelique Kerber              707  
78    Johanna Larsson               702  
79    Melinda Czink                 700  
80    Sofia Arvidsson               685  

RANK  PLAYER                        PTS  
81    Sloane Stephens               659  
82    Anabel Medina Garrigues       650  
83    Kirsten Flipkens              637  
84    Monica Niculescu              623  
85    Carla Suarez Navarro          618  
86    Alla Kudryavtseva             618  
87    Lucie Hradecka                607  
88    Yaroslava Shvedova            599  
89    Kristina Barrois              591  
90    Regina Kulikova               590  
91    Anastasia Rodionova           580  
92    Sorana Cirstea                577  
93    Vesna Dolonts                 576  
94    Eleni Daniilidou              563  
95    Urszula Radwanska             557  
96    Olga Govortsova               534  
97    Arantxa Rus                   523  
98    Anastasiya Yakimova           522  
99    Shuai Zhang                   517  
100   Kai-Chen Chang                512